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A review on time series forecastingtechniques for building energy consumption
Abstract
Energy consumption forecasting for buildings has immense value in energy efficiency and sustainability research. Accurate energy forecasting models have numerous implications in planning and energy optimization of buildings and campuses. For new buildings, where past recorded data is unavailable, computer simulation methods are used for energy analysis and forecasting future scenarios. However, for existing buildings with historically recorded time series energy data, statistical and machine learning techniques have proved to be more accurate and quick. This study presents a comprehensive review of the existing machine learning techniques for forecasting time series energy consumption. Although the emphasis is given to a single time series data analysis, the review is not just limited to it since energy data is often co-analyzed with other time series variables like outdoor weather and indoor environmental conditions. The nine most popular forecasting techniques that are based on the machine learning platform are analyzed. An in-depth review and analysis of the ‘hybrid model’, that combines two or more forecasting techniques is also presented. The various combinations of the hybrid model are found to be the most effective in time series energy forecasting for building.
... Other researchers focused on TSF tasks for specific problems and domains. These studies include the investigation of DL methods for financial time-series [8], and ML techniques for time-series energy consumption forecasting [19] among others. In the manufacturing domain, RUL prediction [20], [21] and regression tasks in predictive maintenance [22] received significant attention among reviews. ...
... Furthermore, feature engineering for time-series data becomes complex, requiring consideration of temporal dependencies, lag effects, and seasonal patterns that may not be easily discernible. Nevertheless, researchers have successfully applied these methods to TSF tasks either directly [19] or as part of a hybrid method [42], demonstrating their relevance despite their limitations and their advantage in specific applications. ...
... In their recommended forecasting approach, Taylor et al. suggest using MAPE due to its simple interpretability [41]. Along with several other studies that have utilized these metrics, the percentage error metrics are widely used across several domains such as computer science [23], energy [19], and economics [112]. ...
TSF is growing in various domains including manufacturing. Although numerous TSF algorithms have been developed recently, the validation and evaluation of algorithms hold substantial value for researchers and practitioners and are missing. This study aims to fill this gap by evaluating the SoTA TSF algorithms on thirteen manufacturing datasets, focusing on their applicability in manufacturing. Each algorithm was selected based on its TSF category to ensure a representative set of algorithms. The evaluation includes different scenarios to evaluate the models using two problem categories and two forecasting horizons. To evaluate the performance, the WAPE was calculated, and additional post hoc analyses were conducted to assess the significance of observed differences. Only algorithms with codes from open-source libraries were utilized, and no hyperparameter tuning was done. This allowed us to evaluate the algorithms as "out-of-the-box" solutions that can be easily implemented, ensuring their usability within the manufacturing by practitioners with limited technical knowledge. This aligns to facilitate the adoption of these techniques in smart manufacturing systems. Based on the results, transformer and MLP-based architectures demonstrated the best performance with MLP-based architecture winning the most scenarios. For univariate TSF, PatchTST emerged as the most robust, particularly for long-term horizons, while for multivariate problems, MLP-based architectures like N-HITS and TiDE showed superior results. The study revealed that simpler algorithms like XGBoost could outperform complex algorithms in certain tasks. These findings challenge the assumption that more sophisticated models produce better results. Additionally, the research highlighted the importance of computational resource considerations, showing variations in runtime and memory usage across different algorithms.
... Time series forecasting is a discipline that permeates almost every facet of society, playing a significant role in both industry and academia. It has countless applications in various fields where it is a key player, such as in economics and financial markets [1,2], climate prediction [3,4], energy consumption [5,6], and medical applications [7,8]. In all these areas, building sufficiently accurate artificial intelligence models is crucial for making future decisions [9]. ...
... 3. Train the neural network 4. Apply ODF2NNA: build a new model by pruning and refining the original model with ϵ network reduction level. 5. Evaluate the simplified model using established benchmarks. ...
Time series forecasting is undoubtedly a key area in machine learning due to the numerous fields where it is crucial to estimate future data points of sequences based on a set of previously observed values. Deep learning has been successfully applied to this area. On the other hand, growing concerns about the steady increase in the amount of resources required by deep learning-based tools have made Green AI gain traction as a move towards making machine learning more sustainable. In this paper, we present a deep learning-based time series forecasting methodology called GreeNNTSF, which aims to reduce the size of the resulting model, thereby diminishing the associated computational and energetic costs without giving up adequate forecasting performance. The methodology, based on the ODF2NNA algorithm, produces models that outperform state-of-the-art techniques not only in terms of prediction accuracy but also in terms of computational costs and memory footprint. To prove this claim, after presenting the main state-of-the-art methods that utilize deep learning for time series forecasting and introducing our methodology we test GreeNNTSF on a selection of real-world forecasting problems that are commonly used as benchmarks, such as SARS-CoV-2 and PhysioNet (medicine), Brazilian Weather (climate), WTI and Electricity (economics), and Traffic (smart cities). The results of each experiment conducted objectively demonstrate, rigorously following the experimentation presented in the original papers that addressed these problems, that our method is more competitive than other state-of-the-art approaches, producing more accurate and efficient models.
... Energy market modelling refers to the development and application of mathematical, computational, and statistical methods used to represent and analyze the dynamics of energy markets [7]. It involves simulating interactions between key participants, such as electricity generators, consumers, regulatory bodies, etc., to forecast market outcomes like prices, demand, and system reliability. ...
This review paper examines the current landscape of electricity market modelling, specifically focusing on stochastic approaches, transitioning from Mean Field Games (MFGs) to Neural Network (NN) modelling. The central objective is to scrutinize and synthesize evolving modelling strategies within power systems, facilitating technological advancements in the contemporary electricity market. This paper emphasizes the assessment of model efficacy, particularly in the context of MFG and NN applications. Our findings shed light on the diversity of models, offering practical insights into their strengths and limitations, thereby providing a valuable resource for researchers, policy makers, and industry practitioners. The review guides navigating and leveraging the latest stochastic modelling techniques for enhanced decision making and improved market operations.
... For predicting future energy demand, based on customers' historical usage of energy, we have chosen Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX) as our proposed model. For energy demand, the most accurate time series analysis model would require taking into consideration both seasonal and exogenous factors [14] Since during seasonal changes, the time series would be non-stationary (i.e. a moving mean), we use Autoregressive Integrated Moving Averages (ARIMAs), or its extended model SARIMAX. We need seasonality due to the increased energy consumption during winter for heating, and summer for cooling, but reduced consumption in spring and autumn. ...
Energy prices have increased by more than 62% globally on average, while power companies are trying to provide more affordable energy with different options: fixed-rate, slab-based tariffs, and Time-of-Use pricing. This article aims to combine blockchains, smart contracts, smart grids, energy forecasting through reinforcement learning, and energy trading between closed communities, bringing consumer savings. Our proposed model, ElectroBlock, uses the first 25 days of each month’s energy usage history along with seasonal and exogenous factors to forecast the final end-of-month usage. Then with the forecast we can predict which customers are the most likely to be pushed to the next energy consumption slab and be charged at a higher rate for the remaining month. This allows these customers to be buyers, and seamlessly buy energy units from users who would sell, the energy units they are predicted to leave unused by the end of the month. Moreover, trading is done with WalletCoins, at a coin per kilowatt hour, but could also be traded directly for fiat currency. Furthermore, the power company makes a profit on every trade through transaction fees. Hence there is great incentive for both customers and power companies to adopt our proposed model. We integrated Hyperledger Fabric into our ElectroBlock prototype to store all customer data and prevent tampering with WalletCoin and consumption records. Finally, we did a performance analysis, on the actual cost savings based on a real-world dataset; and a scalability test for concurrency and customer bases on our prototype.
... Furthermore, by providing facility managers with boundary and target conditions, it enables precise control over a building's energy usage [5]. Additionally, it aids in identifying events associated with system failure or signaling when maintenance is necessary, often by detecting gradual increases in energy consumption over time [6]. Moreover, accurate power load forecasts contribute to estimating a building's occupancy level, thereby assisting in devising optimal control strategies for smart equipment such as lighting and HVAC (Heating, Ventilation, and Air-Conditioning) systems [7]. ...
Efficient energy management relies heavily on accurate load forecasting, particularly in the face of increasing energy demands and the imperative for sustainable operations. However, the presence of anomalies in historical data poses a significant challenge to the effectiveness of forecasting models, potentially leading to suboptimal resource allocation and decision-making. This paper presents an innovative unsupervised feature bank based framework for anomaly detection in time series data affected by anomalies. Leveraging an RNN-based recurrent denoising autoencoder, identified anomalies are replaced with plausible patterns. We evaluate the effectiveness of our methodology through a comprehensive study, comparing the performance of different forecasting models before and after the anomaly detection and imputation processes. Our results demonstrate the versatility and effectiveness of our approach across various energy applications for smart grids and smart buildings, highlighting its potential for widespread adoption in energy management systems.
... Currently, the research field of electricity consumption prediction covers a wide range of algorithms, including statistical methods [12][13][14][15], machine learning [16][17][18][19] and In the diagram, there are five transformers under the distribution system, and there will be some total node components under each transformer. There will be some sub-node components under the master-node components. ...
Electricity consumption prediction is crucial for energy suppliers and industrial companies as it aids in optimizing energy planning and reducing energy consumption losses. Existing methods primarily focus on the time series of individual node or component, overlooking the spatial structure of node groups, which leads to poor prediction accuracy. To overcome this limitation, we propose an autoregressive Kalman filtering (AKF) method for electricity consumption prediction. Our primary contribution is the innovative design of the observation equation in AKF, which adjusts the initial predictions of the autoregressive (AR) model based on the hierarchical structure of components, thereby significantly enhancing prediction accuracy. Specifically, the autoregressive model is utilized to capture the autocorrelation of the sequence, forming the basis for constructing the state equation in AKF. In designing the observation equation, we simplify the model and reduce the complexity of parameter estimation by setting the sum of predicted values of sub-node components as the observed value for the master-node component. To validate the effectiveness of our proposed method, experiments are conducted using real electricity consumption data from Foshan Ceramic Factory. The results demonstrate significant improvements in prediction accuracy compared to baseline methods such as BP, LSTM, GA-BP, PSO-SVM and AR.
... ARIMA models have two notable disadvantages: they require a substantial amount of data smoothing and they are linear [59]. SVM models are challenging to interpret, and finally, ANNs are susceptible to local minima and overfitting issues [60]. In 2017, Facebook released an open-source algorithm for time-series prediction called Prophet [61], which is based on time-series decomposition and is capable of handling outliers related to special events such as holiday periods. ...
As the effects of global climate change intensify, it is increasingly important to imple-
ment more effective water management practices, particularly in arid and semi-arid regions such as Meoqui, Chihuahua, situated in the arid northern center of Mexico. The objective of this study was to identify the optimal time-series model for analyzing the pattern of water extraction volumes and predicting a one-year forecast. It was hypothesized that the volume of water extracted over time could be explained by a statistical time-series model, with the objective of predicting future trends. To achieve this objective, three time-series models were evaluated. To assess the pattern of groundwater extraction, three time-series models were employed: the seasonal autoregressive integrated moving average (SARIMA), Prophet, and Prophet with extreme gradient boosting (XG-Boost). The mean extraction volume for the entire period was 50,935 ± 47,540 m3, with a total of 67,233,578 m3 extracted from all wells. The greatest volume of water extracted has historically been from urban wells, with an average extraction of 55,720 ± 48,865 m3 and a total of 63,520,284 m3. The mean extraction volume for raw water wells was determined to be 20,629 ± 19,767 m3, with a total extraction volume of 3,713,294 m3. The SARIMA(1,1,1)(1,0,0)12 model was identified as the optimal time-series model for general extraction, while a “white noise” model, an ARIMA(0,1,0) for raw water, and an SARIMA(2,1,1)(2,0,0)12 model were identified as optimal for urban wells. These findings serve to reinforce the efficacy of the SARIMA model in forecasting and provide a basis for water resource managers in the region to develop policies that promote sustainable water management.
... With the continuous development of computer technology, traditional time series forecasting methods, often used for short-term univariate predictions, struggle with nonlinear relationships and multivariate, complex data. Consequently, machine learning and data mining techniques have been introduced to address these challenges [8]. These methods analyze data patterns, extract features, and capture correlations through training to predict target events. ...
Atmospheric pollutants’ real-time changes and the internal interactions among various data make it challenging to efficiently predict concentration variations. In order to extract more information from the time series of pollutants and improve the accuracy of prediction models, we propose a type of Multilayer Perceptron model based on wavelet decomposition, named Wavelet Transform-based Multilayer Perceptron (WTMP) model. This model decomposes pollutant data through overlapping discrete wavelet transforms to extract non-stationarity and nonlinear dependencies in the time series. It combines the decomposed data with static covariate information such as data collection time and inputs them into an improved Multilayer Perceptron (MLP) model, reconstructing and outputting the prediction results. Finally, the model is validated using atmospheric pollutant data collected at a specific location in Ruian City, Zhejiang Province, China. The results indicate that the model performs well with minimal prediction errors.
... These methods can be applied to a growing number of available data streams, and have proven to be successful across a range of diverse fields. Examples of these fields include monitoring epidemics [8][9][10][11][12][13] , predicting financial markets [14][15][16][17] , tracking energy consumption 18 and forecasting weather patterns and climate change impacts 19,20 . ...
Early warning systems are an essential tool for effective humanitarian action. Advance warnings on impending disasters facilitate timely and targeted response which help save lives and livelihoods. In this work we present a quantitative methodology to forecast levels of food consumption for 60 consecutive days, at the sub-national level, in four countries: Mali, Nigeria, Syria, and Yemen. The methodology is built on publicly available data from the World Food Programme’s global hunger monitoring system which collects, processes, and displays daily updates on key food security metrics, conflict, weather events, and other drivers of food insecurity. In this study we assessed the performance of various models including Autoregressive Integrated Moving Average (ARIMA), Extreme Gradient Boosting (XGBoost), Long Short Term Memory (LSTM) Network, Convolutional Neural Network (CNN), and Reservoir Computing (RC), by comparing their Root Mean Squared Error (RMSE) metrics. Our findings highlight Reservoir Computing as a particularly well-suited model in the field of food security given both its notable resistance to over-fitting on limited data samples and its efficient training capabilities. The methodology we introduce establishes the groundwork for a global, data-driven early warning system designed to anticipate and detect food insecurity.
... The evolution of data-driven modelling and its rise in usage for forecasting building energy consumption and assessments is due to a significant shift away from traditional statistical models such as ARIMA and SARIMA to advanced Machine Learning (ML) techniques [6,7]. ML methods, notably Deep Learning (DL) techniques, are better suited for the dynamic and intricate energy usage patterns, offering significant improvements in forecasting accuracy and applicability across different temporal and spatial scales in energy planning models [8]. ...
This study investigates the application of Transfer Learning (TL) on Transformer architectures to enhance building energy consumption forecasting. Transformers are a relatively new deep learning architecture, which has served as the foundation for groundbreaking technologies such as ChatGPT. While TL has been studied in the past, these studies considered either one TL strategy or used older deep learning models such as Recurrent Neural Networks or Convolutional Neural Networks. Here, we carry out an extensive empirical study on six different TL strategies and analyse their performance under varying feature spaces. In addition to the vanilla Transformer architecture, we also experiment with Informer and PatchTST, specifically designed for time series forecasting. We use 16 datasets from the Building Data Genome Project 2 to create building energy consumption forecasting models. Experiment results reveal that while TL is generally beneficial, especially when the target domain has no data, careful selection of the exact TL strategy should be made to gain the maximum benefit. This decision largely depends on the feature space properties such as the recorded weather features. We also note that PatchTST outperforms the other two Transformer variants (vanilla Transformer and Informer). We believe our findings would assist researchers in making informed decision in using TL and transformer architectures for building energy consumption forecasting.
... While statistical methods such as Autoregressive Moving Average [11], Autoregressive Integrated Moving Average [12]- [15], and Autoregressive Integrated Moving Average with eXogenous variables [16]- [18] have been used for forecasting energy consumption in buildings, they depend on restrictive assumptions such as linearity and stationary input data (constant statistical properties over time). These limitations have led to the exploration of machine learning (ML) techniques, algorithms that learn from data to make predictions without explicit programming, thereby overcoming such constraints and demonstrating growing interest in this field [19]- [22]. ...
As energy demands and costs rise, enhancing energy efficiency in Food and Drink Cold Storage (FDCS) rooms is important for reducing expenses and achieving environmental sustainability ambitions. Forecasting electricity use in FDCSs can help optimise operations and minimise energy consumption by enabling door opening frequency, maintenance, and restocking to be better scheduled. Although Machine Learning (ML) has been applied to forecast energy use in various domains such as commercial and residential buildings, its use in addressing the specific challenges of FDCS, which require stringent temperature and humidity control for food safety and quality, has been less explored. This work addresses this gap by proposing a tailored ML pipeline for FDCS settings capable of predicting one-week into the future and is suitable for small dataset sizes. It provides comparative analysis by employing two distinct real-world FDCS datasets for training, validation, and testing of the developed models. Moreover, in contrast to existing studies predominantly concerned with energy consumption prediction, this study includes the forecasting of indoor temperature and humidity, given their essential role in preserving the quality and longevity of stored food items. Ensemble-based methods, particularly Random Forest, excelled and achieved the lowest electricity MAEs of 150.65 and 384.88 for each dataset, respectively.
... The term "building energy prediction" refers to using models to predict future building energy uses. When applied to existing buildings that have historical time series energy data recorded, machine learning (ML) techniques have shown to be more rapid and accurate (Deb et al., 2017). Artificial intelligence's machine learning (ML) technology allows computers to learn without explicit programming (Iranmanesh & Kaveh, 1999). ...
Reducing a building’s energy use has many real-world applications. An early-stage design could have a quantitative foundation for energy-saving designs if energy consumption could be predicted quickly and accurately. The main issue that designers are currently dealing with is the incompatibility of building modelling and energy simulation software. In order to realize the flexibility of building energy systems, accurate and timely thermal load prediction for buildings is essential. Here, three machine learning (ML) models – Artificial Neural Network (ANN), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) were used, for forecasting an office building’s load demand and energy usage. A case study building was selected and analysed via Autodesk Revit and Green Building Studio. For the modelling of ANN, 438 simulated data samples were created based on different design parameters considering different window, wall, roof materials and window to wall ratio, and meteorological conditions considering dew point, dry bulb, wet bulb temperature and relative humidity of seven major cities in Bangladesh. The findings show that the ANN model performs best the best in predicting annual electricity use with an R² value of 0.991 and annual load demand with an R² value of 0.995. The RMSE values ranged between 3.83 and 5.10 showing high accuracy of prediction between the three ML models. Afterwards SHAP analysis was used to analyse the input features effect on the energy consumption. Findings show that relative humidity, dry bulb temperature and pressure significantly affects the energy consumption.
... In this way, a robust correlation exists between cementitious materials' physicochemical properties and time. 44 However, addressing regression problems with a substantial time correlation using traditional machine learning models may neglect the inherent temporal correlation, contradicting the strong internal time correlation characteristic observed in hydration kinetics. 45,46 To tackle this challenge, some researchers have employed time-series models such as Long Short-Term Memory (LSTM) to explore correlations between time variables. ...
An innovative method is proposed in this study that combines long sequence precision prediction model with experiments to study how early hydration affects ultra‐low water‐binder ratio cementitious composite (ULWBRCC) properties. To be specific, by using heat release and degree of hydration as variables and introducing various temporal models to analyze the early hydration process, the micro‐ and macroscopic properties of specimens at different ages are assessed in detail. The obtained results can be summarized as follows: The long‐sequence accurate time series model can predict the earliest hydration process of underwater ULWBRCC, and the predicted late‐stage hydration pattern matches the experimental results. The underwater environment optimizes ULWBRCC's hydration process and increases its early compressive strength by 19.6%. The hydration of ULWBRCC is predicted using a time series model in the paper, filling the gap of uncertainty in the earliest performance of underwater rapid repair cement‐based composite materials, and the MSE of the Informer is improved by 92.88% compared with other models.
... These techniques leverage past data to model and predict future material prices, offering insights into potential price trends and fluctuations. They often fall under the categories of "machine learning" and "deep learning" and have been actively applied to relevant studies over the last two decades [19][20][21]. Presently, the data-driven forecasting models in the field of construction economics can broadly be classified into two categories: causal modeling and time-series analysis. ...
The construction industry is heavily influenced by the volatility of material prices, which can significantly impact project costs and budgeting accuracy. Traditional econometric methods have been challenged by their inability to capture the frequent fluctuations in construction material prices. This paper reviews the application of data-driven techniques, particularly machine learning, in forecasting construction material prices. The models are categorized into causal modeling and time-series analysis, and characteristics, adaptability, and insights derived from large datasets are discussed. Causal models, such as multiple linear regression (MLR), artificial neural networks (ANN), and the least square support vector machine (LSSVM), generally utilize economic indicators to predict prices. The commonly used economic indicators include but are not limited to the consumer price index (CPI), producer price index (PPI), and gross domestic product (GDP). On the other hand, time-series models rely on historical price data to identify patterns for future forecasting, and their main advantage is demanding minimal data inputs for model calibration. Other techniques are also explored, such as Monte Carlo simulation, for both price forecasting and uncertainty quantification. The paper recommends hybrid models, which combine various forecasting techniques and deep learning-advanced time-series analysis and have the potential to offer more accurate and reliable price predictions with appropriate modeling processes, enabling better decision-making and cost management in construction projects.
... Time series forecasting is essential for analyzing natural and social systems . Previous studies have adopted various machine learning (ML) algorithms to predict future trends in various fields (Deb et al., 2017;Taieb et al., 2012). Shabbir et al. (Shabbir & Chaturvedi, 2022) used random forest and seasonal autoregressive integrated moving average (ARIMA) to model the historical time series. ...
Functional magnetic resonance imaging (fMRI) is currently one of the most popular technologies for measuring brain activity in both research and clinical contexts. However, clinical constraints often result in short fMRI scan durations, limiting the diagnostic performance for brain disorders. To address this limitation, we developed an end‐to‐end frequency‐specific dual‐attention‐based adversarial network (FDAA‐Net) to extend the time series of existing blood oxygen level‐dependent (BOLD) data, enhancing their diagnostic utility. Our approach leverages the frequency‐dependent nature of fMRI signals using variational mode decomposition (VMD), which adaptively tracks brain activity across different frequency bands. We integrated the generative adversarial network (GAN) with a spatial–temporal attention mechanism to fully capture relationships among spatially distributed brain regions and temporally continuous time windows. We also introduced a novel loss function to estimate the upward and downward trends of each frequency component. We validated FDAA‐Net on the Human Connectome Project (HCP) database by comparing the original and predicted time series of brain regions in the default mode network (DMN), a key network activated during rest. FDAA‐Net effectively overcame linear frequency‐specific challenges and outperformed other popular prediction models. Test–retest reliability experiments demonstrated high consistency between the functional connectivity of predicted outcomes and targets. Furthermore, we examined the clinical applicability of FDAA‐Net using short‐term fMRI data from individuals with autism spectrum disorder (ASD) and major depressive disorder (MDD). The model achieved a maximum predicted sequence length of 40% of the original scan durations. The prolonged time series improved diagnostic performance by 8.0% for ASD and 11.3% for MDD compared with the original sequences. These findings highlight the potential of fMRI time series prediction to enhance diagnostic power of brain disorders in short fMRI scans.
... Forecasting energy consumption is a complex task in time series analysis. Data gathered by smart sensors frequently include redundancy, missing values, outliers, and uncertainties [6], which complicates the prediction of electrical energy usage with conventional methods due to the unpredictable nature of energy consumption trends, including regular seasonal patterns [4,7]. To maximize buildings' energy efficiency, appropriate operational approaches need to be incorporated into energy control schemes [8]. ...
Power consumption in the home has grown in recent years as a consequence of the use of varied residential applications. On the other hand, many families are beginning to use renewable energy, such as energy production, energy storage devices, and electric vehicles. As a result, estimating household power demand is necessary for energy consumption monitoring and planning. Power consumption forecasting is a challenging time series prediction topic. Furthermore, conventional forecasting approaches make it difficult to anticipate electric power consumption since it comprises irregular trend components, such as regular seasonal fluctuations. To address this issue, algorithms combining stationary wavelet transform (SWT) with deep learning models have been proposed. The denoised series is fitted with various benchmark models, including Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), Bidirectional Gated Recurrent Units (Bi-GRUs), Bidirectional Long Short-Term Memory (Bi-LSTM), and Bidirectional Gated Recurrent Units Long Short-Term Memory (Bi-GRU LSTM) models. The performance of the SWT approach is evaluated using power consumption data at three different time intervals (1 min, 15 min, and 1 h). The performance of these models is evaluated using metrics such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The SWT/GRU model, utilizing the bior2.4 filter at level 1, has emerged as a highly reliable option for precise power consumption forecasting across various time intervals. It is observed that the bior2.4/GRU model has enhanced accuracy by over 60% compared to the deep learning model alone across all accuracy measures. The findings clearly highlight the success of the SWT denoising technique with the bior2.4 filter in improving the power consumption prediction accuracy.
... The complexity is further compounded when striving to establish meticulous design and physical models. However, with the evolution of data operation and maintenance systems, the realm of load forecasting in the Building Cooling Load (BCL) domain has witnessed a paradigm shift towards the pervasive adoption of data-driven methodologies (Deb et al., 2017). ...
Prediction of building cooling load: an innovative design for GA-SVM and IG-SVM system for the estimation of hourly precision and computational fluctuation range, Advances in Building Energy Research, ABSTRACT Anticipating the loads that buildings will bear is a crucial strategy in the pursuit of energy efficiency and the reduction of emissions. In this paper, we introduce an interactive and comprehensive joint prediction model, specifically crafted to elevate the precision of forecasts related to building loads. The resultant Genetic Algorithm-Support Vector Machine (GA-SVM) prediction model is put into action to provide hourly predictions of cooling loads for buildings. In scenarios where the prediction of building cooling load (BCL) fluctuations becomes imperative, especially in the face of extreme weather conditions, the information granulation (IG) method is employed. The findings of this validation process unveil significant improvements. The coefficient of variation of root mean square error (CV-RMSE) and mean absolute percentage error (MAPE) for the GA-SVM model are reduced by 58.85% and 68.04%, respectively, when compared to the conventional SVM model. Furthermore, in a comparative analysis against three widely utilized prediction models, the SVM model demonstrates a reduction in CV-RMSE and MAPE ranging from 2.04% to 68.04%. The application of the joint prediction model showcases impressive results, with R 2 values ranging from 97.27% to 99.68%, MAPE ranging from 2.59% to 2.84%, and CV-RMSE ranging from only 0.0249 to 0.0319.
... Specifically, it is a sequence of observations recorded in successive time points that may either be continuous or discrete. Time series can be found across many disciplines, including meteorology [1,2], econometrics [3,4], energy consumption [5,6], retail sales [7,8], healthcare [9,10], transportation [11,12], and marketing [13,14]. Time series can be classified into the following categories: univariate and multivariate. ...
Time series forecasting is crucial in various domains, ranging from finance and economics to weather prediction and supply chain management. Traditional statistical methods and machine learning models have been widely used for this task. However, they often face limitations in capturing complex temporal dependencies and handling multivariate time series data. In recent years, deep learning models have emerged as a promising solution for overcoming these limitations. This paper investigates how deep learning, specifically hybrid models, can enhance time series forecasting and address the shortcomings of traditional approaches. This dual capability handles intricate variable interdependencies and non-stationarities in multivariate forecasting. Our results show that the hybrid models achieved lower error rates and higher R2 values, signifying their superior predictive performance and generalization capabilities. These architectures effectively extract spatial features and temporal dynamics in multivariate time series by combining convolutional and recurrent modules. This study evaluates deep learning models, specifically hybrid architectures, for multivariate time series forecasting. On two real-world datasets - Traffic Volume and Air Quality - the TCN-BiLSTM model achieved the best overall performance. For Traffic Volume, the TCN-BiLSTM model achieved an R2 score of 0.976, and for Air Quality, it reached an R2 score of 0.94. These results highlight the model’s effectiveness in leveraging the strengths of Temporal Convolutional Networks (TCNs) for capturing multi-scale temporal patterns and Bidirectional Long Short-Term Memory (BiLSTMs) for retaining contextual information, thereby enhancing the accuracy of time series forecasting.
Electricity price prediction has an imperative role in the UK energy market among energy trading organisations. The price prediction directly impacts organisational policy for profitable electricity trading, better bidding plans, and the optimisation of energy storage devices for any surplus energy. Business organisations always look for the use of price-prediction models with higher accuracy to help them maximise benefits. With the enhancement of Internet of Things (IoT) technology, data availability on energy demand, and hence the associated price prediction modelling has become more effective tools than before. However, price prediction has been a challenging task because of the uncertainty in the demand and supply and other external factors such as weather, and gas prices as these factors can influence the fluctuation of electricity prices. In this regard, the selection of an appropriate prediction model is crucial for business organisations. In this paper, an analytical study has been presented to predict short-term electricity prices in the UK market as a use case for a UK-based energy trading company. ARIMA, Prophet, XGBoost as well as Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long-Short Term Memory (LSTM) algorithms have been analysed. In this study, UK Market Index Data (MID) from Elexon API data has been used that represent half-hourly electricity prices. In addition, gas prices, and initial demand out-turn data, as external factors, are introduced into the models for improving the accuracy and performance of these models. The comparative analysis shows that the ARIMA can handle only one external factor in its prediction model, while the Prophet and XGBoost can incorporate multiple external regressors in their models. Also, the models based on deep learning algorithms can deal with univariate and multivariate time series. The comparative analysis also revealed that the XGBoost model has better performance than the ARIMA and Prophet models, as has been found in earlier studies. With the extended analysis, it has been found that deep learning models outperform ARIMA, Prophet, and XGBoost models in terms of prediction accuracy. This extended comparative analysis gives the flexibility to choose the appropriate model selection for any organisation working in analogous business scenarios as of the business use case of this study.
The management of energy market data, such as load, production, forecasts, and prices, is critical for energy market participants, who develop in-house energy data infrastructure services to aggregate data from many sources to support their business operations. Energy data management frequently involves time sensitive operations, including rapid data ingestion, real-time querying, filling in gaps from missing or delayed data, and updating large volumes of timestamped and loosely structured data, all of which demand high processing power. Traditional relational database management systems (RDBMSs) often struggle with these operations, whereas time series databases (TSDBs) appear to be a more efficient solution, providing enhanced scalability, reliability, real-time data availability and superior performance. This paper examines the advantages of TSDBs over RDBMS for energy data management, demonstrating that TSDBs can either replace or complement RDBMSs. We present quantitative improvements in digestion, integration, architecture, and performance, demonstrating that operations such as importing and querying time-series energy data, along with the overall system’s efficiency, can be significantly improved, achieving up to 100 times faster operations compared to relational databases, all without requiring extensive modifications to the existing information system’s architecture.
In this study, we predict inventory for an IoT-enabled vending machine warehouse servicing approximately 1,500 vending machines with the goal of timely replenishing, achieving cost effectiveness, reducing stock waste, optimising the available resources and ensuring fulfilment of consumer demand. The study deploys four different ML algorithms, namely, Extreme gradient boosting, Autoregressive integrated moving average with/without exogenous variables (ARIMA/ARIMAX), Facebook Prophet (Fb Prophet), and Support Vector Regression (SVR). The study unfolds in two phases. First, we utilise conventional historical sales data variables to make the prediction whereas in the second phase, we systematically introduced external variables including weekday, sales deviation flag, and holiday flags into our ML algorithms. The results indicate a significant performance boost using external variables with extreme gradient boosting achieving the lowest (Mean Absolute Error) MAE of 22, followed by ARIMAX, FB Prophet, and SVR with MAE values of 27, 37, and 38, respectively.
This study aims to explore the application of artificial intelligence (AI) in the resolution of sustainability challenges, with a specific focus on environmental studies. Given the rapidly evolving nature of this field, there is an urgent need for more frequent and dynamic reviews to keep pace with the innovative applications of AI. Through a systematic analysis of 191 research articles, we classified AI techniques applied in the field of sustainability. Our review found that 65% of the studies applied supervised learning methods, 18% employed unsupervised learning, and 17% utilized reinforcement learning approaches. The review highlights that artificial neural networks (ANN), are the most commonly applied AI techniques in sustainability contexts, accounting for 23% of the reviewed methods. This comprehensive overview of AI techniques identifies key trends and proposes new research avenues to address the complex issue of achieving the Sustainable Development Goals (SDGs).
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Microgrids are a development trend and have attracted a lot of attention worldwide. The control system plays a crucial role in implementing these systems and, due to their complexity, artificial intelligence techniques represent some enabling technologies for their future development and success. In this paper, we propose a novel formulation of an economic model predictive control (economic MPC) applied to a microgrid designed for a faculty building with the inclusion of a predictive model to deal with the energy demand disturbance using a recurrent neural network of the long short‐term memory (RNN‐LSTM). First, we develop a framework to identify an RNN‐LSTM using historical data registered by a smart three‐phase power quality analyzer to provide feedforward power demand predictions. Next, we present an economic MPC formulation that includes the prediction model for the disturbance within the optimization problem to be solved by the MPC strategy. We carried out simulations with different scenarios of energy consumption, available resources, and simulation times to highlight the results obtained and analyze the performance of the energy management system. In all cases, we observed the correct operation of the proposed control scheme, complying at all times with the objectives and operational restrictions imposed on the system.
In the past, those working in the industry believed maintenance was a secondary function. Therefore it was carried out only in ways Primitive and dominated by the traditional nature, which is “breakdown repair” or what is called corrective maintenance. Then the management of the industrial facility in question will pay many times the amount of money saved if it employed the correct maintenance methods of all kinds on time. Given the exorbitant capital financial cost of the components of industrial facilities that are spent until the project is
18 | African Journal of Advanced Pure and Applied Sciences (AJAPAS)
completed, it is self-evident that those components are preserved by performing maintenance on time. This leads to a reduction in malfunctions and an increase in its lifespan.
Published scientific research has revealed that the CBM (condition-based maintenance) method is considered an effective method in detecting many hidden defects in many mechanisms and their applied elements used in industry at an early stage. In this paper, was discussed due to its importance and An attempt to uncover its secrets and apply them to some mechanical parts and equipment in Ras Lanuf Company, where the focus was on the technique of analyzing vibrations in rotating equipment using a system and devices for analyzing and measuring vibrations and dynamic balancing devices, on several points specifically in the seawater intake area of Ras Lanuf Company, Several readings were taken for a centrifugal pump on the ground in the company.
Studies confirm the importance of using vibration analysis technology in maintenance, which increases the quality of the production process because maintenance is considered one of the elements of quality according to international standards, and all of this will increase the production volume and reduce the cost of the material produced.
Load forecasting is a crucial task, which is carried out by utility companies for sake of power grids' successful planning, optimized operation and control, enhanced performance, and guaranteed reliability and adequacy. It mainly reduces the gap between demand and supply and thus assures an uninterrupted power supply and load shedding free operation. The electricity demand can be forecasted by several Artificial Intelligence (AI), Deep Learning (DL), and Machine Learning (ML) approaches of which hybrid methods are the most famous. This paper examines various present technologies and algorithms of load forecasting including digital twin (DT), data-mining (DM), federated learning (FL), and transfer learning (TL). In this work, a comprehensive assessment of forecasting with single and hybrid models is performed, their functions are reviewed and pros and cons are investigated. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE) are used for performance assessment and comparison of the different models searching for best load forecasting model. The role of different experts and intelligent systems used for optimal load forecasting in power systems will be clearly elaborated along with their pros and cons, thus providing a roadmap research path for the readers, who wants to contribute to the scientific society related to this research field.
Buildings tend to not operate as intended, and a pronounced gap often exists between measured and predicted environmental and energy performance. Although the causes of this ‘performance gap’ are multi-faceted, issues surrounding data integration are key contributory factors. The distributed nature of the Architecture, Engineering and Construction (AEC) industry presents many challenges to the effective capture, integration and assessment of building performance data. Not all building data can be described semantically, nor is it feasible to create adapters between many different software tools. Similarly, not all building contextual data can easily be captured in a single product-centric model.
Buildings have in recent years been the target of a number of energy efficiency improvement strategies given that they are a major energy end-use sector in most countries. Whilst new buildings due to legislations, increasingly address sustainability and improved energy efficiency considerations, the refurbishment process of older buildings still presents a number of challenges. Advancement in Information and Communication Technology, particularly the application of low-cost Wireless Sensors and Actuators Network does however provide the opportunity to harness yet unrealized energy reduction in existing buildings. This paper presents results from an experimental study evaluating the performance and energy saving potentials of such off-the-shelve, low-cost wireless sensors and actuators network in an existing office building for occupancy detection and occupancy-driven lighting control. The study demonstrates that in addition to improved occupancy information obtainable from Wireless Sensors and Actuators Network, worthwhile savings in the energy consumption of the lighting systems can as well be achieved.
The purposes of this research are to find a model to forecast the electricity consumption in a household and to find the most suitable forecasting period whether it should be in daily, weekly, monthly, or quarterly. The time series data in our study was individual household electric power consumption from December 2006 to November 2010. The data analysis has been performed with the ARIMA (Autoregressive Integrated Moving Average) and ARMA (Autoregressive Moving Average) models. The suitable forecasting methods and the most suitable forecasting period were chosen by considering the smallest value of AIC (Akaike Information Criterion) and RMSE (Root Mean Square Error), respectively. The result of the study showed that the ARIMA model was the best model for finding the most suitable forecasting period in monthly and quarterly. On the other hand, ARMA model was the best model for finding the most suitable forecasting period in daily and weekly. Then, we calculated the most suitable forecasting period and it showed that the method were suitable for short term as 28 days, 5 weeks, 6 months and 2 quarters, respectively.
Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.
Predicting short term electricity load accurately is critical to facilitate demand side management in the building sector. For buildings that have electricity sub-metering systems installed, it is possible to predict both the total electricity load and the loads of individual building service systems (air conditioning, lighting, power, and other equipment). In this paper, a method based on Support Vector Machine (SVM) is proposed to predict the loads at system level. For each type of system, 24 SVM models (one model per hour) were trained and deployed to predict the hourly electricity load. The inputs for the prediction method are simply weather predictions and hourly electricity loads in two previous days. A case study shows that the proposed method outperforms three other popular data mining methods (ARIMAX, Decision Tree, and Artificial Neural Network) in both CV_RMSE and N_MBE. Thus, SVM method is suggested for predicting system level electricity loads of public buildings.
The aim of this study is to provide a precise model for one-month-ahead forecast of electricity demand in residential sector. In this study, a total of 20 influential variables are taken into account including monthly electricity consumption, 14 weather variables, and 5 social variables. Based on support vector regression and fuzzy-rough feature selection with particle swarm optimization algorithms, the proposed method established a model with variables that relate to the forecast without ignoring some of these variables one may inevitably lead to forecasting errors. The proposed forecasting model was validated using historical data from South Korea. Its time period was from January 1991 to December 2012. The first 240 months were used for training and the remaining 24 for testing. The performance was evaluated using MAPE, MAE, RMSE, MBE, and UPA values. Furthermore, it was compared with that obtained from the artificial neural network, auto-regressive integrated moving average, multiple linear regression models, and the methods proposed in the previous studies, and found superior for every performance measure considered in this study. The proposed method has an advantage over the previous methods because it automatically determines appropriate and necessary variables for a reliable forecast. It is expected that the proposed model can contribute to more accurate forecasting of short-term electricity demand in residential sector. The ability to accurately forecast short-term electricity demand can assist power system operators and market participants in ensuring sustainable electricity planning decisions and secure electricity supply to the consumers.
This paper tests and compares two types of modelling to predict the same time series. A time series of electric load was observed and, as a case study, we opted for the metropolitan region of Bahia State. The combination of three exogenous variables were attempted in each model. The exogenous variables are: the number of customers connected to the electricity distribution network, the temperature and the precipitation of rain. The linear model time series forecasting used was a SARIMAX. The modelling of computational intelligence used to predict the time series was a Fuzzy Inference System. According to the evaluation of the attempts, the Fuzzy forecasting system presented the lowest error. But among the smallest errors, the results of the attempts also indicated different exogenous variables for each forecast model.
The importance of the residential class in the consumption of electricity in the Brazilian Electric System (BES) can be recognized by its quantitative size, as it, in 2013, concentrates 27% of the total consumption and 85% among all consumers. Also, in this class are the main public policies such as subsidies for consumer units inhabited by low-income families, labelling and increased energy efficiency of appliances used in the home and others. This paper aims to model and forecast the Brazilian residential energy consumption, up to 2050, with Pegels exponential smoothing techniques. In addition to the forecasts with the best model in sample, an optimization procedure of the model's hyper parameters is carried out in order to adjust the projections provided by the Energy Research Company (ERC). The results obtained show that it is possible to predict satisfactorily the electricity consumption for the proposed horizon with minimum error in sample. And the exercise of optimization proved to be important for providing level and trend equations for the official expectations regarding the residential electricity consumption in Brazil.
As a popular data driven method, artificial neural networks (ANNs) have been widely applied in building energy prediction field for decades. To improve the short term prediction accuracy, this paper presents a kind of optimized ANN model for hourly prediction of building electricity consumption. An improved Particle Swarm Optimization algorithm (iPSO) is applied to adjust ANN structure's weights and threshold values. The principal component analysis (PCA) is used to select the significant modeling inputs and simplify the model structure. The investigation utilizes two different historical data sets in hourly interval, which are collected from the Energy Prediction Shootout contest I and a campus building located in East China. For performance comparison, another two prediction models, ANN model and hybrid Genetic Algorithm-ANN (GA-ANN) model are also constructed in this study. The comparison results reveal that both iPSO-ANN and GA-ANN models have better accuracy than that of ANN ones. From the perspective of time consuming, the iPSO-ANN model has shorter modeling time than GA-ANN method. The proposed prediction model can be thought as an alternative technique for online prediction tasks of building electricity consumption.
Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.
This study presents building energy forecasting methodology for cooling load for three institutional buildings. These buildings belong to a university campus in Singapore. The daily energy consumption for cooling load is obtained for a period of two years and the daily variation is analyzed. The energy consumption is initially divided into five classes and the class numbers are used as inputs to develop a forecasting model. The model is developed using two machine learning tools. The tools used are Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Interface System (ANFIS). The division of data for training and testing the model is nearly 60% and 40% respectively. The results show that both ANN and ANFIS forecast the cooling load energy consumption of the three buildings with good accuracy. The correlation coefficient between measured and predicted consumption for training data are well above 0.98. The same is well above 0.96 for testing data. It is noted that such a methodology can be positively extended to other institutional buildings in the campus.
Short-term load forecasting (STLF) plays a decisive role in electric power system operation and planning. Accurate load forecasting not only reduces the generation costs of power systems, but also serves to maximize profit for participants in electricity markets. In recent years, power markets have grown more deregulated and competitive, adding to the complexity and uncertainties of load, and making it more difficult for conventional techniques to accurately forecast the load. To improve the accuracy of load forecasting, this paper suggests a hybrid method, called Gray-Fuzzy-Markov Chain Method (GFMCM), comprising three stages. In the first stage, daily load is forecasted by Gray model, with its training deviations classified, in a second stage, by fuzzy-set theory, and finally, fed into Markov chain model to predict future relative errors that might be supplied by the Gray model. The proposed approach has been verified by the historical data of power consumption in Ontario, PJM and Iranian electricity markets. The obtained forecasts by GFMCM proved to have better prediction properties compared to the other forecasting techniques, such as Gray models, specifically GM(1,1) and GM(1,2), ARIMA time series, wavelet-ARIMA and multi-layer perceptron (MLP) neural network.
This study proposes using a random forest model for short-term electricity load forecasting. This is an ensemble learning method that generates many regression trees (CART) and aggregates their results. The model operates on patterns of the time series seasonal cycles which simplifies the forecasting problem especially when a time series exhibits nonstationarity, heteroscedasticity, trend and multiple seasonal cycles. The main advantages of the model are its ability to generalization, built-in cross-validation and low sensitivity to parameter values. As an illustration, the proposed forecasting model is applied to historical load data in Poland and its performance is compared with some alternative models such as CART, ARIMA, exponential smoothing and neural networks. Application examples confirm good properties of the model and its high accuracy.
Demand planning for electricity consumption is a key success factor for the development of any countries. However, this can only be achieved if the demand is forecasted accurately. In this research, different forecasting methods-autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and multiple linear regression (MLR)-were utilized to formulate prediction models of the electricity demand in Thailand. The objective was to compare the performance of these three approaches and the empirical data used in this study was the historical data regarding the electricity demand (population, gross domestic product: GDP, stock index, revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010. The results showed that the ANN model reduced the mean absolute percentage error (MAPE) to 0.996%, while those of ARIMA and MLR were 2.80981 and 3.2604527%, respectively. Based on these error measures, the results indicated that the ANN approach outperformed the ARIMA and MLR methods in this scenario. However, the paired test indicated that there was no significant difference among these methods at α = 0.05. According to the principle of parsimony, the ARIMA and MLR models might be preferable to the ANN one because of their simple structure and competitive performance.
Electricity load forecasting plays a key role in operation of power systems. Since the penetration of distributed and renewable generation is increasingly growing in many countries, Short-Term Load Forecast (STLF) of micro-grids is also becoming an important task. A precise STLF of the micro-grid can enhance the management of its renewable and conventional resources and improve the economics of energy trade with electricity markets. As a consequence of the highly non-smooth and volatile behavior of the load time series in a micro-grid, its STLF is even a more complex process than that of a power system. For this purpose, a new prediction method is proposed in this paper, in which a Self-Recurrent Wavelet Neural Network (SRWNN) is applied as the forecast engine. Moreover, the Levenberg-Marquardt (LM) learning algorithm is implemented and adapted to train the SRWNN. In order to demonstrate the efficiency of the proposed method, it is examined on real-world hourly data of an educational building within a micro-grid. Comparisons with other load prediction methods are provided.
One of the largest threats to future development is climate change. Apparently, the building sector has been the largest source of greenhouse gas production. One prospective solution to this is “green building” that aims to provide environmentally sustainable building in terms of design, construction and maintenance. However, green buildings symbolize the next stage of buildings, and the recent growth of new green building constructions is inadequate to overcome the negative impact of existing buildings. One logical solution to reduce the environmental impact of the existing buildings is through green retrofitting. Yet there is lack of systematic review on the existing body of knowledge on green retrofitting which is critical for future research. This paper aims to critically review the existing literature on green retrofitting and to identify contemporary research trends. Additionally, with the view to the current challenges, barriers, obstacles or problems to green retrofitting, this study highlights the needs to identify the Critical Success Factors (CSFs) for successful implementation of green retrofit projects.
It is important for building owners and operators to manage the electrical energy consumption of their buildings. As electrical energy is the major form of energy consumed in a commercial building, the ability to forecast electrical energy consumption in a building will bring great benefits to the building owners and operators. This paper provides a review of the building electrical energy consumption forecasting methods which include the conventional and artificial intelligence (AI) methods. The significant goal of this study is to review, recognize, and analyse the performance of both methods for forecasting of electrical energy consumption. Compared to using a single method of forecasting, the hybrid of two forecasting methods can possibly be applied for more precise results. Regarding this potential, the swarm intelligence (SI) method has been reviewed to be hybridized with AI. Published literature presented in this paper shows that, the hybrid of SVM and SI methods has indeed presented superior performance for forecasting building electrical energy consumption.
The pressing global environmental issues are fostering a rapid change in the energy and sustainability policies for the built environment. New paradigms are emerging, such as “Nearly Zero Energy Building” (nZEB), and resource efficiency is progressively becoming a crucial topic in the building sector, implying an appropriate consideration of performance over the whole life cycle. However, empirical evidences show how, very often, the gap between the predicted (design phase) and measured (operation phase) performance is very large, due to errors committed during all the phases of building life cycle. This performance gap determines a problem of credibility in the building industry and, more in general, in sustainability oriented practices. Therefore, design and operation practices should evolve in order to be able to cope with performance uncertainty determined, for example, by evolution of climate conditions, variability of behavioural patterns and performance degradation of technological components.
Building energy use prediction plays an important role in building energy management and conservation as it can help us to evaluate building energy efficiency, conduct building commissioning, and detect and diagnose building system faults. Building energy prediction can be broadly classified into engineering, Artificial Intelligence (AI) based, and hybrid approaches. While engineering and hybrid approaches use thermodynamic equations to estimate energy use, the AI-based approach uses historical data to predict future energy use under constraints. Owing to the ease of use and adaptability to seek optimal solutions in a rapid manner, the AI-based approach has gained popularity in recent years. For this reason and to discuss recent developments in the AI-based approaches for building energy use prediction, this paper conducts an in-depth review of single AI-based methods such as multiple linear regression, artificial neural networks, and support vector regression, and ensemble prediction method that, by combining multiple single AI-based prediction models improves the prediction accuracy manifold. This paper elaborates the principles, applications, advantages and limitations of these AI-based prediction methods and concludes with a discussion on the future directions of the research on AI-based methods for building energy use prediction.
In this paper, we focus on the prediction method of building energy consumption time series. The building energy consumption data can be regarded as a time series, which is usually nonlinear and non-stationary. Traditional time series analysis model has lower prediction accuracy. Then the machine learning method, especially support vector regression algorithm always has better performance to deal with non-stationary and nonlinear time series. So the support vector regression algorithm is applied to develop building energy consumption time series model. The model is applied in different buildings. Experimental results show the prediction accuracy of the model is better than traditional time series analysis model.
The intelligent building, a major smart city research and development domain, has grown beyond the scope of automation, currently focusing on the occupant centered approaches and the ancillary services offered to the power grid. The Internet of Things (IoT) introduced in the recent years are enabling those prospects. However, due to numerous legacy automation systems already in the construction market, the IoT based intelligent building approach is frequently challenged. This paper assesses the opportunities along with the criticism for a fully IoT enabled and controllable intelligent building against the well established, legacy automation systems in a fair and transparent approach. In the second part, it proposes an interoperable intelligent building design for the creation of advanced building managements schemes, by integrating the assets of current automation tools and the emerging innovations. This unification and the progressive phasing out of legacy technologies, would catalyze the market of intelligent buildings in the context of smart cities by not limiting their potential solely on new constructions and by mitigating the investment if legacy systems are already installed.
Building intelligent system has become one of the standard configurations in a public building even in a residential building. Intelligent buildings are expected to provide high-efficient, convenient, comfortable and energy-saving built environment so as to bring additional benefits. However, problems like sensor faults and control strategy flaws may result in low performance and high energy consumptions and maintenance costs are needed as well. Thus it is needed to synthetically evaluate the costs and benefits of intelligent systems to help building investors to make decisions. This paper proposes a cost-benefit evaluation method for building intelligent systems. Life cycle net present value (NPV) of all the costs and benefits, including tangible and intangible, is used as an index to evaluate the performance of the building intelligent systems. The proposed method puts emphases on the quantitative evaluation of intangible benefits using Analytical Hierarchy Process (AHP) method to determine the weights of each aspects of intangible benefit. The building energy consumptions are taken as an another emphasis as well. The proposed method is applied to evaluate three actual buildings to check its feasibility and effectiveness. Sensitivity analysis of NPV to energy consumption of the case study buildings shows that the energy consumption increase or decrease caused by different performances of intelligent systems can influence the positive or negative of NPV, which implies the building intelligent systems are worth of investment or not.
Electricity load forecasting is crucial for effective operation and management of buildings. Support Vector Regression (SVR) have been successfully used in solving nonlinear regression and time series problems related to building energy consumption forecasting. As the performance of SVR heavily depends on the selection of its parameters, differential evolution (DE) algorithm is employed in this study to solve this problem. The forecasting model is developed using weighted SVR models with nu-SVR and epsilon-SVR. The DE algorithm is again used to determine the weights corresponding to each model. A case of time series energy consumption data from an institutional building in Singapore is used to elucidate the performance of the proposed model. The proposed model can be used to forecast both, half-hourly and daily electricity consumption time series data for the same building. The results show that for half-hourly data, the model exhibits higher weight for nu-SVR, whereas for daily data, a higher weight for epsilon-SVR is observed. The mean absolute percentage error (MAPE) for daily energy consumption data is 5.843 and that for half-hourly energy consumption is 3.767 respectively. A detailed comparison with other evolutionary algorithms show that the proposed model yields higher accuracy for building energy consumption forecasting.
Household air pollution is ranked the 9th largest Global Burden of Disease risk (Forouzanfar et al., The Lancet 2015). People, particularly urban dwellers, typically spend over 90% of their daily time indoors, where levels of air pollution often surpass those of outdoor environments. Indoor air quality (IAQ) standards and approaches for assessment and control of indoor air require measurements of pollutant concentrations and thermal comfort using conventional instruments. However, the outcomes of such measurements are usually averages over long integrated time periods, which become available after the exposure has already occurred. Moreover, conventional monitoring is generally incapable of addressing temporal and spatial heterogeneity of indoor air pollution, or providing information on peak exposures that occur when specific indoor sources are in operation. This article provides a review of the new air pollution sensing methods to determine IAQ and discusses how real-time sensing could bring a paradigm shift in controlling the concentration of key air pollutants in billions of urban houses worldwide. However, we also show that besides the opportunities, challenges still remain in terms of maturing technologies, or data mining and their interpretation. Moreover, we discuss further research and essential development needed to close gaps between what is available today and needed tomorrow. In particular, we demonstrate that awareness of IAQ risks and availability of appropriate regulation are lagging behind the technologies.
Traditionally, short-term electricity price forecasting has been essential for utilities and generation companies. However, the deregulation of electricity markets created a competitive environment and the introduction of new market participants, such as the retailers and aggregators, whose economic viability and profitability highly depends on the spot market price patterns. The aim of this study is to examine artificial neural network (ANN) based models for Day-ahead price forecasting. Specifically, the models refer to the sole application of ANNs or to hybrid models, where the ANN is combined with clustering algorithm. The training data are clustered in homogenous groups and for each cluster, a dedicated forecaster is employed. The proposed models are characterized by comprehensive operation and by high level of flexibility; different inputs can be taken under consideration and different ANN topologies can be examined. The models are tested on a data set that consists of atypical price patterns and many outliers. This approach makes the price forecasting problem a more challenging task, providing evidence that the proposed models can be considered as useful and robust forecasting tools to the actual needs of market participants, including the traditional generation companies and self-producers, but also the retailers/suppliers and aggregators.
A mixture of support vector machines (SVMs) is proposed for time series forecasting. The SVM mixture is composed of a two-stage architecture. In the first stage, self-organizing feature map (SOM) is used as a clustering algorithm to partition the whole input space into several disjointed regions. A tree-structured architecture is adopted in the partition to avoid the problem of predetermining the number of partitioned regions. Then, in the second stake, multiple SVMs, also called SVM mixture, that best fit partitioned regions are constructed by finding the most appropriate kernel function and the optimal free parameters of SVMs. The experiment shows that the SVMs mixture achieves significant improvement in the generalization performance in comparison-with the single SVMs model. In addition, the SVMs mixture also converges faster and use fewer support vectors.
Titanium alloys such as TÎ6A14V have been popular choices as bulk materials for loadbearing orthopedic implants due to their resistance to corrosion and excellent biocompatibility. To enhance the biological properties and wear resistance of TÎ6A14V, samples of the titanium alloy were tested against titanium composites formed with TÎ6A14V with 2 and 5wt. % tricalcium phosphate. Results show that the calcium phosphate tends to form along the grain boundaries of the titanium alloy matrix, which causes the surface hardness to rise from an average of 177 HV to as high as 542 HV. The increased strength of the material also significantly reduced the amount of material removed during wear testing and preserved a relatively smooth wear track surface to reduce frictional forces from shear. Contact angle measurements with distilled water decreased as the concentration of TCP increased, implying greater hydrophilicity with the composite materials. Simulated body fluid testing for three days show that the presence of TCP in the material accelerated the growth rate of apatite crystals. The results from the contact angle measurements along with the SBF study show that the TCP composite can enhance biological responses of the composites within the body.
This paper study the used of moving averages (MA) and Exponential smoothing techniques (EST) for load forecasting. The case study was in Universiti Teknologi PETRONAS (UTP),Malaysia. The study was divided by two types of load forecasting namely Semester On (SOn)and Semester Off (SOf). Later, MA and ESMT being used to forecast the usage load for bothSOn and SOf. The results indicated that ESMT gives better forecasting compared to MA in terms of less measurements of error e.g. Mean Absolute Percentage Error (MAPE).
The enhancement of load forecasting has become one of the core research topics in the energy field. Because power load has both time-variant and nonlinear characteristics, different types of methods, neural networks (NN) in particular, have been applied to power load forecasting. This study proposes a real-valued genetic algorithm (RGA)- based neural network with support vector machine (NN-SVM) model to predict the power load in both short-term and mid-term forecasting by using a radial-basis-function neural network (RBFNN), SVM and RGA. The model consists of two stages. In short-term load forecasting (STLF), the first stage applies the RBFNN to predict monthly variations, and the second stage trains the SVM through hourly data to obtain the final forecast. Similar operations are used in mid-term load forecasting (MTLF). In the process of SVM training and NN learning, RGA is used to find the optimal parameters. The results of several experiments show that this new model performs more accurately and stably than some conventional models including RBFNN, RGA-SVM, Karman filter in STLF. Also it is able to function well in MTLF.
This paper presents a detailed investigation and analysis of the energy consumption characteristics of three institutional buildings in Singapore. Building information, energy consumption data of the air-conditioning system, and energy consumption data of the plug loads were collected separately. Identification models are developed to predict the real daily energy consumption data. Developed models involve three specific functions to represent the variability of the daily occupancy, the additional occupancy due to visitors and the variation of outdoor air temperature. The performance of the developed identification model is very satisfactory and fits very well with the real energy consumption data. Based on the identification model, key factors which are influencing the energy consumption in institutional buildings are identified as the variability of the daily occupancy, hence a methodology is developed to calculate the occupancy in the building. The identified parameters are used as inputs into deterministic energy simulation programs, like Energy Plus, to perform detailed energy analysis. The whole methodology developed has improved significantly the accuracy of the energy simulation modelling of institutional buildings and has permitted to understand and evaluate the major energy characteristics of these buildings.
In this paper five exponential smoothing methods are considered for load forecasting for lead times from a half-hour-ahead to a year-ahead. Forecasting load demand with high accuracy is required to prevent energy wasting and system failure. For this purpose, a half-hourly load demand of Malaysia for one year, from September 01, 2005 to August 31, 2006 was used. The mean absolute percentage error (MAPE) is used for comparing the forecasting accuracy. Time series of load demand recorded at half-hourly intervals contains more than one seasonal pattern, which are the intraday and intraweek seasonal cycles. The forecasts produced by the Holt-Winters Taylor (HWT) exponential smoothing outperformed the traditional Holt-Winters and modified Holt-Winters exponential smoothing methods.
Short-term load forecasting of building electricity usage is of great importance for anomaly detection on electricity usage pattern and management of building energy consumption in an environment where electricity pricing is dynamically determined based on the peak energy consumption. In this paper, we present a data-driven forecasting model for day-ahead electricity usage of buildings in 15-minute resolution.
By using variable importance analysis, we have selected key variables: day type indicator, time-of-day, HVAC set temperature schedule, outdoor air dry-bulb temperature, and outdoor humidity as the most important predictors for electricity consumption. This study proposes a short-term building energy usage forecasting model based on an Artificial Neural Network (ANN) model with Bayesian regularization algorithm and investigates how the network design parameters such as time delay, number of hidden neurons, and training data effect on the model capability and generality.
The results demonstrate that the proposed model with adaptive training methods is capable to predict the electricity consumption with 15-minute time intervals and the daily peak electricity usage reasonably well in a test case of a commercial building complex.
Electric load forecasting is an important issue for power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents a SVR model hybridized with the differential empirical mode decomposition (DEMD) method and auto regression (AR) for electric load forecasting. The differential EMD method is used to decompose the electric load into several detail parts associated with high frequencies (intrinsic mode function (IMF)) and an approximate part associated with low frequencies. The electric load data from the New South Wales (NSW, Australia) market and the New York Independent System Operator (NYISO, USA) are employed for comparing the forecasting performances of different alternative models. The results illustrate the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.