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Load Prediction Based on Multivariate Time Series Forecasting for Energy Consumption and Behavioral Analytics
Abstract
Liable, proficient and ecological cognizant electrical energy consumption activities are becoming a basic need for the consistent smart grid. This paper put forwards the concept of a data mining and intelligent proposed model to scrutinize including predict electrical energy time sequences to discover a number of time-based power consumption patterns. Support Vector Regression (SVR) have been productively employed to resolve non-linear regression and time sequences complications associated with prediction of residential electric energy consumption. Jaya algorithm is used in this paper as the implementation of SVR is greatly reliant on the collection of its constraints. The predicting model is technologically advanced by means of weighted SVR configurations (ν-SVR and-SVR). Besides, the Jaya algorithm is deployed to decide the weights resultant to every configuration. An instance of time sequential power consumption information from a residential edifice in Denmark is employed to explicate the execution of the presented configuration. Furthermore , the anticipated model is able to estimate power consumption for half hour and daily time successions data for the similar building. The consequences depict that the proposed model demonstrates developed weight for ν-SVR for half hour data. Nevertheless, a sophisticated weight for-SVR is perceived for diurnal data. The Mean Absolute Percentage Error (MAPE) for everyday power expenditure data is 5.521 while for half-hour power utilization is 3.769 correspondingly. Also, a thorough evaluation with different algorithms indicate that the presented configuration produces greater exactness for residential power exhaustion prediction.
... These sensors generate lots of data in the form of multivariate time series (MTS). A key task in the analysis and mining of these data is multivariate time series classification (MTSC), which aims to give an accurate response to a large number of problems: e.g. from detecting when a patient is sick or has an anomaly in his heart behavior [23], or if a driver is in optimal condition to drive [22], the recognition of human activities [29] or how to adapt energy production based on particular circumstances [20]. ...
... To avoid the use of variables without information, we analyzed the training set looking for variables with a single value. If any variable with this condition is found, it is eliminated from both the training set and the test set (Lines [16][17][18][19][20][21]. Finally, each of the specified models is processed, obtaining the desired model fit, its prediction on the test set and the accuracy achieved (Lines 23-28). ...
Multivariate time series classification is a task with increasing importance due to the proliferation of new problems in various fields (economy, health, energy, transport, crops, etc.) where a large number of information sources are available. Direct extrapolation of methods that traditionally worked in univariate environments cannot frequently be applied to obtain the best results in multivariate problems. This is mainly due to the inability of these methods to capture the relationships between the different variables that conform a multivariate time series. The multivariate proposals published to date offer competitive results but are hard to interpret. In this paper we propose a time series classification method that considers an alternative representation of time series through a set of descriptive features taking into account the relationships between the different variables of a multivariate time series. We have applied traditional classification algorithms obtaining interpretable and competitive results.
... The systematic and proficient utilization of electrical power is a hot debate topic in today's world [9]. The optimal power management and maintaining balance between demand and supply are considered as challenging tasks for modern power systems [10]. Moreover, the prediction of uncertain production of renewable energy resources [11] and short-term load forecasting [12] are measured as significant components of the power grid for optimal power scheduling. ...
... If d n 0 ,1 and d n 0 ,2 are greater than 0, then Λ n 0 ,2 = Λ n 0 ,3 = 0. So, Equations (8) and (9) are generalized as: d n,uc 0 = γ n 0 − (τ n 0 κ uc 0 Λ n 0 ,1 ), (10) where n 0 ∈ n and uc 0 = 1, 2, ..., n. Now, using Equation (6) in Equation (10), ...
Demand Response Management (DRM) is considered one of the crucial aspects of the smart grid as it helps to lessen the production cost of electricity and utility bills. DRM becomes a fascinating research area when numerous utility companies are involved and their announced prices reflect consumer’s behavior. This paper discusses a Stackelberg game plan between consumers and utility companies for efficient energy management. For this purpose, analytical consequences (unique solution) for the Stackelberg equilibrium are derived. Besides this, this paper presents a distributed algorithm which converges for consumers and utilities. Moreover, different power consumption activities on the basis of time series are becoming a basic need for load prediction in smart grid. Load forecasting is taken as the significant concerns in the power systems and energy management with growing technology. The better precision of load forecasting minimizes the operational costs and enhances the scheduling of the power system. The literature has discussed different techniques for demand load forecasting like neural networks, fuzzy methods, Naïve Bayes, and regression based techniques. This paper presents a novel knowledge based system for short-term load forecasting. The algorithms of Affinity Propagation and Binary Firefly Algorithm are integrated in knowledge based system. Besides, the proposed system has minimum operational time as compared to other techniques used in the paper. Moreover, the precision of the proposed model is improved by a different priority index to select similar days. The similarity in climate and date proximity are considered all together in this index. Furthermore, the whole system is distributed in sub-systems (regions) to measure the consequences of temperature. Additionally, the predicted load of the entire system is evaluated by the combination of all predicted outcomes from all regions. The paper employs the proposed knowledge based system on real time data. The proposed scheme is compared with Deep Belief Network and Fuzzy Local Linear Model Tree in terms of accuracy and operational cost. In addition, the presented system outperforms other techniques used in the paper and also decreases the Mean Absolute Percentage Error (MAPE) on a yearly basis. Furthermore, the novel knowledge based system gives more efficient outcomes for demand load forecasting.
... However, these long-and short-term temporal patterns are now easily analysed, learned, and predicted with accuracy, thanks to the development of machine learning and deep learning algorithms powered and fueled up by big data IoT devices. Tese algorithms have been useful in many felds, including forecasting of trafc [25][26][27], energy use [28], stock market analysis [29], pandemic outbreak [30], sales analysis [31], and price prediction [32] trafc volume, occupancy, or fow is taken into account, it will be classifed as univariate time series forecasting. But in a larger sense, if data from several locations are used to analyse trafc fow and its association, the problem is a standard multivariate time series forecasting one. ...
The timely and accurate forecasting of urban road traffic is crucial for smart city traffic management and control. It can assist both drivers and traffic controllers in selecting efficient routes and diverting traffic to less congested roads. However, estimating traffic volume while taking into account external factors such as weather and accidents is still a challenge. In this research, we propose a hybrid deep learning framework, double attention graph neural network BiLSTM (DAGNBL), that utilizes a graph neural network to represent spatial characteristics and bidirectional LSTM units to capture temporal dependencies between features. Attention modules are added to the GNN and BLSTM to find high-impact attention weight values for the chosen road section. Our model offers the best prediction accuracy with a mean absolute percentage error of 5.21% and a root mean squared error of 4. It can be utilized as a useful tool for predicting traffic flow on certain stretches of road.
... Xiao et al. [7] proposed the Multi-scale Skip Deep Long Short-Term Memory (MSD-LSTM) model for short-term load prediction with multivariate data. Khan et al. [8] applied SVR to realize multivariate time series forecasting model for load prediction. ...
The construction of smart grids has greatly changed the power grid pattern and power supply structure. For the power system, reasonable power planning and demand response is necessary to ensure the stable operation of a society. Accurate load prediction is the basis for realizing demand response for the power system. This paper proposes a Pre-Attention-CNN-GRU model (PreAttCG) which combines a convolutional neural network (CNN) and gate recurrent unit (GRU) and applies the attention mechanism in front of the whole model. The PreAttCG model accepts historical load data and more than nine other factors (including temperature, wind speed, humidity, etc.) as input. The attention layer and CNN layer effectively extract the features and weights of each factor. Load forecasting is then performed by the prediction layer, which consists of a stacked GRU. The model is verified by industrial load data from a German dataset and a Chinese dataset from the real world. The results show that the PreAttCG model has better performance (3~5% improvement in MAPE) than both LSTM with only load input and LSTM with all factors. Additionally, the experiments also show that the attention mechanism can effectively extract the weights of relevant factors affecting the load data.
... Time series forecasting is an important problem in data mining with many real-world applications including finance [1]- [4], weather forecasting [5], [6], power consumption monitoring [7], [8], industrial maintenance [9], [10], occupancy monitoring in smart buildings [11], [12], and many others. Recently, deep learning (DL) models showed tremendous success in analyzing time series data [1], [13] when compared to the other traditional methods. ...
Multivariate time series (MTS) regression tasks are common in many real-world data mining applications including finance, cybersecurity, energy, healthcare, prognostics, and many others. Due to the tremendous success of deep learning (DL) algorithms in various domains including image recognition and computer vision, researchers started adopting these techniques for solving MTS data mining problems, many of which are targeted for safety-critical and cost-critical applications. Unfortunately, DL algorithms are known for their susceptibility to adversarial examples which also makes the DL regression models for MTS forecasting also vulnerable to those attacks. To the best of our knowledge, no previous work has explored the vulnerability of DL MTS regression models to adversarial time series examples, which is an important step, specifically when the forecasting from such models is used in safety-critical and cost-critical applications. In this work, we leverage existing adversarial attack generation techniques from the image classification domain and craft adversarial multivariate time series examples for three state-of-the-art deep learning regression models, specifically Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). We evaluate our study using Google stock and household power consumption dataset. The obtained results show that all the evaluated DL regression models are vulnerable to adversarial attacks, transferable, and thus can lead to catastrophic consequences in safety-critical and cost-critical domains, such as energy and finance.
... Time series forecasting is an important problem in data mining with many real-world applications including finance [1]- [4], weather forecasting [5], [6], power consumption monitoring [7], [8], industrial maintenance [9], [10], occupancy monitoring in smart buildings [11], [12], and many others. Recently, deep learning (DL) models showed tremendous success in analyzing time series data [1], [13] when compared to the other traditional methods. ...
Multivariate time series (MTS) regression tasks are common in many real-world data mining applications including finance, cybersecurity, energy, healthcare, prognostics, and many others. Due to the tremendous success of deep learning (DL) algorithms in various domains including image recognition and computer vision, researchers started adopting these techniques for solving MTS data mining problems, many of which are targeted for safety-critical and cost-critical applications. Unfortunately, DL algorithms are known for their susceptibility to adversarial examples which also makes the DL regression models for MTS forecasting also vulnerable to those attacks. To the best of our knowledge, no previous work has explored the vulnerability of DL MTS regression models to adversarial time series examples, which is an important step, specifically when the forecasting from such models is used in safety-critical and cost-critical applications. In this work, we leverage existing adversarial attack generation techniques from the image classification domain and craft adversarial multivariate time series examples for three state-of-the-art deep learning regression models, specifically Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). We evaluate our study using Google stock and household power consumption dataset. The obtained results show that all the evaluated DL regression models are vulnerable to adversarial attacks, transferable, and thus can lead to catastrophic consequences in safety-critical and cost-critical domains, such as energy and finance.
... Price forecasting There is a need to discuss the redundancy of features. Bayesian Network [21] Power Consumption Short and Medium Term Accuracy in half an hour or daily consumption Cannot forecast more than one day or week ahead. ...
... Jaya algorithm in [17] is used for load forecasting. Authors in [18] used Bayesian Network to predict power consumption and user behavior. However, this work only forecast the load for one day. ...
... Different forecasting techniques,data set,features and limitations are use in related work.The purpose of related work is to minimize the cost in term of electricity that is use for forecasting.In this section their are two types of method are use statistical and Machine learning.In statistical Auto Regressive Integrated Moving average(ARIMA) is use as whereas in Machine learning Support Vector Machine(SVM) is use.In paper [2],CNN and LSTM is use as a classifier.Type of forecasting is price and data set is PJM.Author in [3] ELM and WNN apply techniques,forecasting type is price and data set is AEMO.In [4] apply technique is DNN for both load and price forecasting and data set that is use NYISO.In paper [5],DNN is use as a classifier.Type of forecasting is price and load and data set is EPEX.In [6],Price forecasting is use as a predication,SVM is use a classifier and data set is New England and Australian.Author [7],DRN is a classifier,data set is ISONE and type of forecasting is load.In paper [8],combination of GCA and Random Forest are use as a classifier and data set is ISO NE CA and type of forecasting is price.In paper [9],ARMAX is use as a classifier,forecasting is price,data set is AEMO.Author in [10],use combination of MI and IG use as a classifier,type of forecasting and data set is PJM.In paper [11], variant of MI ANN is use a classifier,type of forecasting is load and data set is PJM.In paper [12],LSTM GA is a technique,forecasting is load and data set is Metropolitan electricity consumption.Author in [13],CNN LSTM use as a classifier,forecasting is price and data set is International Exchange.In paper [14],variant of ANN and SVR is use as a classifier,predication is load forecasting and data set is Energy consumption.Author in [15],ARIMA is apply as a classifier,forecasting is price and data set is PJM.In paper [16],Bayesian Network is a classifier,Denmark time series energy load ahead is dataset and forecasting is laod and price. ...
Accurate and real-time traffic flow forecasting plays an important role in optimizing traffic routing enabling adaptive and sophisticated applications on the network. Managing and routing enormous traffic flow with dynamic behavior is a highly challenging task. However, arriving at a precise model for traffic forecasting in a short interval of time is not trivial because of the dynamic nature of traffic flow. A novel multivariate time series framework is designed to analyze and forecast the dynamic traffic flow in SDN based networks. The proposed framework adapts the Multivariate Singular Spectrum Analysis (MSSA) forecasting model and incorporates the Randomized Singular Value Decomposition (RSVD) to improve the accuracy of flow prediction. Simulations are conducted to evaluate the effectiveness of the proposed MSSA method. The proposed method predicts the long-term traffic fluctuation from the observed traffic traces. The SDN controller is trained using the traffic traces and future traffic flows are forecasted. The performance evaluation of the proposed method predicts real-time traffic trends accurately with 2.2% MAPE, 9.44 MAE and 13.803 RMSE. The results show that the learning ability of MSSA helps to forecast future network traffic with low prediction errors.
Energy consumption forecasting based on IoT data and deep learning algorithm inheriting distributed and collaborative learning is a widely studied topic both in engineering and computer science fields. For different households with drastically different energy consumption patterns, the traditional centralized machine learning (ML) and deep learning (DL) methods suffer problems including inaccuracy, inefficiency and laggings of the prediction performance. In this study, we propose a sophisticated multi-channel bidirectional nested LSTM framework (MC-BiNLSTM) combined with discrete stationary wavelet transform (SWT) for highly accurate and efficient energy consumption forecasting. The main contributions of this study include the decomposition using SWT for accuracy improvement and the collaborative BiNLSTM structure for efficiency improvement. A real-world IoT energy consumption dataset, named UK-DALE, is adopted for the comparative study. The experimental results showed the outperformance of the proposed method from various perspectives over the cutting-edge methods existed in the literature.
Multivariate time series classification is a machine learning task with increasing importance due to the proliferation of information sources in different domains (economy, health, energy, crops, etc.). Univariate methods lack the ability to capture the relationships between the different variables that compose a multivariate time series and therefore cannot be directly extrapolated to multivariate environments. Despite the good performance and competitive results of the multivariate proposals published to date, they are hard to interpret due to their high complexity. In this paper, we propose a multivariate time series classification method based on an alternative representation of the time series, composed of a set of 41 descriptive time series features, in order to improve the interpretability of time series and results obtained. Our proposal uses traditional classifiers over the extracted features to look for relationships between the different variables that form a multivariate time series. We have selected four state-of-the-art algorithms as base classifiers to evaluate our method. We have tested our proposal on the complete University of East Anglia repository, obtaining highly interpretable results capable of explaining the relationships between the features that compose the time series and achieving performance results statistically indistinguishable from the best algorithms of the state-of-the-art.
The rapid depletion of natural sources of energy, coupled with increasing global population has triggered the emergence of various techniques and strategies for building energy consumption prediction. According to information from existing body of knowledge, this paper systematically brings to fore the application areas of building energy consumption prediction (i.e. well-established and emerging), the relationships between these areas and the ways in which authors integrate the current spate of techniques. Based on direct implications of buildings on global energy consumption and CO2 emissions, this information makes it possible to identify trends, strengths and limitations in this context, thereby enabling the centralisation of activities required for future studies. This study follows several well-documented guides for conducting logical reviews of primary articles concerning main topics of building energy consumption prediction within popular online databases. The definition of articles’ search keywords as well as inclusion/exemption factors were governed by a combination of principles stipulated by Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Procedures for Performing Systematic Reviews (PPSR). In comparison to existing review articles in the studied field, the current study is novel in the sense that it provides a very holistic view to building energy consumption prediction, thereby minimising the need to consult multiple individualised studies that are limited to specific techniques, data sets, regions or types of buildings. Another unique feature of this study is its interrelationship network of articles which depicts a quick glance at some of the most influential studies as well as underrepresented areas, thereby aiding research planning, future directions and cross-disciplinary collaborations.
As time is a precious asset, bus riders would desire to get accurate information about bus arrival time. Although different research approaches have been developed to correctly predict bus arrival time, very few of them produce highly precise and accurate results based on open data. In this paper, we present an intelligent system designed for transportation analytics on open data such as bus delay data. Specifically, the system accesses open data to analyze public transport data—such as historical bus arrival time—for urban analytics; it then conducts data analytics and mining to discover frequent patterns. Based on the discovered patterns, the system makes predictions on whether the bus arrives on time or is being late. Evaluation on real-life open data provided by a Canadian city show the effectiveness and prediction accuracy of our intelligent system in transportation analytics on open data. The results are encouraging towards the goal of developing smart cities.
The prevailing situations of water crisis i.e. nonavailability of drinking water, unpredicted floods, rapidly changing paths of water streams, are of great concern. The death rate is increasing day by day, because of the low quality of drinking water in most regions of the world. Similarly, the situations of flooding also cause huge losses from time to time. Many attempts are being made by researchers for the detection and monitoring of water quality and flow to overcome the uncertainties associated with the quality of drinking water available to the general public and early warning of floods by adopting computing techniques. This paper aims to give the overview of data sources and techniques being used by existing literature and attempts to classify and highlight the ways of data gathering for water quality and flow. The real-time and reliable data for detection of water quality and flow for making predictions is difficult to collect. Many limitations have been attached to the predictions made i.e. location dependency. This study guides the researcher and provides insights to the researchers about the possible ways and sources of data that can be utilized by keeping tradeoffs in consideration.
Responsible, efficient and environmentally aware energy consumption behavior is becoming a necessity for the reliable modern electricity grid. In this paper, we present an intelligent data mining model to analyze, forecast and visualize energy time series to uncover various temporal energy consumption patterns. These patterns define the appliance usage in terms of association with time such as hour of the day, period of the day, weekday, week, month and season of the year as well as appliance-appliance associations in a household, which are key factors to infer and analyze the impact of consumers’ energy consumption behavior and energy forecasting trend. This is challenging since it is not trivial to determine the multiple relationships among different appliances usage from concurrent streams of data. Also, it is difficult to derive accurate relationships between interval-based events where multiple appliance usages persist for some duration. To overcome these challenges, we propose unsupervised data clustering and frequent pattern mining analysis on energy time series, and Bayesian network prediction for energy usage forecasting. We perform extensive experiments using real-world context-rich smart meter datasets. The accuracy results of identifying appliance usage patterns using the proposed model outperformed Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) at each stage while attaining a combined accuracy of 81.82%, 85.90%, 89.58% for 25%, 50% and 75% of the training data size respectively. Moreover, we achieved energy consumption forecast accuracies of 81.89% for short-term (hourly) and 75.88%, 79.23%, 74.74%, and 72.81% for the long-term; i.e., day, week, month, and season respectively.
In this paper we evaluate the impact of knowledge about environmental and energy issues on potential pro-environmental behaviour in households, specifically relating to behaviours, attitudes and habits towards energy use. Our results are based on an empirical survey and we find significant correlations which indicate that residents with positive environmental values and greater environmental knowledge are more likely to demonstrate energy behaviours, attitudes and habits which lead to energy saving activities in households. This is further supported through a Principal Component Analysis (PCA), which suggests that energy saving behaviour may also vary according to gender and employment status. Conversely, we find only limited evidence of statistical associations between environmental predisposition and knowledge, and ownership and frequency of use of household appliances. We argue that our results contribute to the significant body of literature supporting the role of knowledge in active engagement with energy issues. This study is timely following closely policy developments in active consumer engagement by the European Commission.
Demand response programs are currently being proposed as a solution to deal with issues related to peak demand and to improve the operation of the electric power system. In the demand response paradigm, electric utilities provide incentives and benefits to private consumers as a compensation for their flexibility in the timing of their electricity consumption. In this paper, a dynamic energy management framework, based on highly resolved energy consumption models, is used to simulate automated residential demand response. The models estimate the residential demand using a novel bottom-up approach that quantifies consumer energy use behavior, thus providing an accurate estimation of the actual amount of controllable resources. The optimal schedule of all of the controllable appliances, including plug-in electric vehicles, is found by minimizing consumer electricity-related expenditures. Recently, time-varying electricity rate plans have been proposed by electric utilities as an incentive to their customers with the objective of re-shaping the aggregate demand. Large-scale simulations are performed to analyze and quantitatively assess the impact of demand response programs using different electricity price structures. Results show that simple time-varying electricity price structures, coupled with large-scale adoption of automated energy management systems, might create pronounced rebound peaks in the aggregate residential demand. To cope with the rebound peaks created by the synchronization of the individual residential demands, innovative electricity price structures—called Multi-TOU and Multi-CPP—are proposed.
Energy management for residential homes and offices require the prediction of the usage(s) or service request(s) of different appliances present in the house. The hardware requirement is more simplified and practical if the task is only based on energy consumption data and no other sensors are used. The proposed model tries to formalize such an approach using a time-series based multi-label classifier which takes into account correlation between different appliances among other factors. In this work, prediction results are shown for 1-hour in the future but this approach can be extended to predict more hours in the future as per the requirement(with restrictions). The learned models and decision tree showing the important factors in the input information is also discussed.
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.
Accurate electric load forecasting could prove to be a very useful tool for all market participants in electricity markets. Because it can not only help power producers and consumers make their plans but also can maximize their profits. In this paper, a new combined forecasting method (ESPLSSVM) based on empirical mode decomposition, seasonal adjustment, particle swarm optimization (PSO) and least squares support vector machine (LSSVM) model is proposed. In the electric market, noise signals usually affect the forecasting accuracy, which were caused by different erratic factors. First of all, ESPLSSVM uses an empirical mode decomposition-based signal filtering method to reduce the influence of noise signals. Secondly, ESPLSSVM eliminates the seasonal components from the de-noised resulting series and then it models the resultant series using the LSSVM which is optimized by PSO (PLSSVM). Finally, by multiplying the seasonal indexes by the PLSSVM forecasts, ESPLSSVM acquires the final forecasting result. The effectiveness of the presented method is examined by comparing with different methods including basic LSSVM (LSSVM), empirical mode decomposition-based signal filtering method processed by LSSVM (ELSSVM) and seasonal adjustment processed by LSSVM (SLSSVM). Case studies show ESPLSSVM performed better than the other three load forecasting approaches.
Managing energy in the home is key to creating a sustainable future for our society. More tools are increasingly available to measure home energy usage, however these tools provide little insight into questions such as why an appliance consumes more energy than normal or what kinds of behavioral changes might be most likely to reduce energy usage in the home. To answer these questions, a deeper understanding of the causal factors that influence energy usage is necessary. In this work, we conduct a broad study of factors that influence energy consumption of individual devices in the home. Our first contribution is collection of a context-rich data set from six homes across the United States. The second contribution of this work is a set of insights into key factors influencing energy usage derived by the novel application of a rule mining algorithm to identify significant associations between energy usage and four key features: hour of the day, day of the week, use of other appliances in the home, and user-supplied annotations of activities such as working or cooking. Our analysis confirms our hypothesis that, though most devices show a regular pattern of daily or weekly use, this is not true for all devices. Associations that relate use of two different devices in the same home are often stronger, and are observed for nearly 25% of device uses. Overall, we observe that the associations derived from the first five weeks of data in our data set are sufficient to explain nearly 70% of the device uses in the subsequent five weeks of data, and over 90% of the associations identified during the first five weeks recur in the latter portion of the data set. The associations identified by our approach may be used to to aid in end-user applications that heighten awareness and encourage energy savings, improve energy disaggregation algorithms, or even detect anomalous uses that may signal problems in aging-in-place homes.
Accurate annual power load forecasting can provide reliable guidance for power grid operation and power construction planning, which is also important for the sustainable development of electric power industry. The annual power load forecasting is a non-linear problem because the load curve shows a non-linear characteristic. Generalized regression neural network (GRNN) has been proven to be effective in dealing with the non-linear problems, but it is very regretfully finds that the GRNN have rarely been applied to the annual power load forecasting. Therefore, the GRNN was used for annual power load forecasting in this paper. However, how to determine the appropriate spread parameter in using the GRNN for power load forecasting is a key point. In this paper, a hybrid annual power load forecasting model combining fruit fly optimization algorithm (FOA) and generalized regression neural network was proposed to solve this problem, where the FOA was used to automatically select the appropriate spread parameter value for the GRNN power load forecasting model. The effectiveness of this proposed hybrid model was proved by two experiment simulations, which both show that the proposed hybrid model outperforms the GRNN model with default parameter, GRNN model with particle swarm optimization (PSOGRNN), least squares support vector machine with simulated annealing algorithm (SALSSVM), and the ordinary least squares linear regression (OLS_LR) forecasting models in the annual power load forecasting.
Annual load forecasting is very important for the electric power industry. As influenced by various factors, an annual load curve shows a non-linear characteristic, which demonstrates that the annual load forecasting is a non-linear problem. Support vector regression (SVR) is proven to be useful in dealing with non-linear forecasting problems in recent years. The key point in using SVR for forecasting is how to determine the appropriate parameters. This paper proposes a hybrid load forecasting model combining differential evolution (DE) algorithm and support vector regression to deal with this problem, where the DE algorithm is used to choose the appropriate parameters for the SVR load forecasting model. The effectiveness of this model has been proved by the final simulation which shows that the proposed model outperforms the SVR model with default parameters, back propagation artificial neural network (BPNN) and regression forecasting models in the annual load forecasting.
The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, the operation of sub-level components like lighting and HVAC systems, occupancy and their behavior. This complex situation makes it very difficult to accurately implement the prediction of building energy consumption. This paper reviews recently developed models for solving this problem, which include elaborate and simplified engineering methods, statistical methods and artificial intelligence methods. Previous research work concerning these models and relevant applications are introduced. Based on the analysis of previous work, further prospects are proposed for additional research reference.
This paper discusses how neural networks, applied to predict energy consumption in buildings, can advantageously be improved, guided by statistical procedures, such as hypothesis testing, information criteria and cross validation. Recent literature has provided evidence that such methods, commonly used independently, when exploited together, can improve the selection and estimation of neural models.We use such an approach to design feed forward neural networks for modeling energy use and predicting hourly load profiles, where both the relevance of input variables and the number of free parameters are systematically treated. The model building process is divided in three parts: (a) the identification of all potential relevant input, (b) the selection of hidden units for this preliminary set of inputs, through an additive phase and (c) the remove of irrelevant inputs and useless hidden units through a subtractive phase.The predictive performance of short term predictors is also examined with regard to prediction horizon. A comparison of the predictive ability of a single-step predictor iteratively used to predict 24 h ahead and a 24-step independently designed predictor is presented.The performance of the developed models and predictors was evaluated using two different data sets, the energy use data of the Energy Prediction Shootout I contest, and of an office building, located in Athens. The results show that statistical analysis as an integral part of neural models, gives a valuable tool to design simple, yet efficient neural models for building energy applications.
Accurately electric load forecasting has become the most important management goal, however, electric load often presents nonlinear data patterns. Therefore, a rigid forecasting approach with strong general nonlinear mapping capabilities is essential. Support vector regression (SVR) applies the structural risk minimization principle to minimize an upper bound of the generalization errors, rather than minimizing the training errors which are used by ANNs. The purpose of this paper is to present a SVR model with immune algorithm (IA) to forecast the electric loads, IA is applied to the parameter determine of SVR model. The empirical results indicate that the SVR model with IA (SVRIA) results in better forecasting performance than the other methods, namely SVMG, regression model, and ANN model.
The literature concerning the application of information-feedback methods for saving energy in the home is reviewed. Particular attention is given to electronic feedback via smart meters and displays, or “energy-consumption indicators” (ECI). Previous studies have not focused on individual appliances, but this paper presents the findings of a UK field study involving 44 households which considered domestic cooking: it compares the effectiveness of providing paper-based energy-use/saving information with electronic feedback of energy-consumption via ECIs designed specifically for this investigation. Twelve Control Group households were monitored for a period of at least 12 months and this revealed an average daily consumption for electric cooking of 1.30 kWh. Subsequently across a minimum monitoring period of 2 months, 14 out of 31 households achieved energy savings of greater than 10% and six of these achieved savings of greater than 20%. The average reduction for households employing an ECI was 15%, whereas those given antecedent information alone reduced their electricity consumption, on average, by only 3%. The associated behavioural changes and the importance of providing regular feedback during use are identified. It is recommended that further attention be given to optimising the design and assessing the use of energy-consumption indicators in the home, in order to maximise the associated energy-saving potential.
This paper deals with the problem of the user behavior prediction in a home automation system. Anticipating the needed energy
for a service is based on the available prediction (like user requests) which contains the uncertainties. When the future
users requests are not available in a home automation system thanks to programmatic, it is interesting to predict it to anticipate
the energy needed in order to avoid some problems like peak consumption. A general method to predict users requests for services
in energy consumption is proposed. The method relies on Bayesian networks to predict and diagnose user’s behavior in housing.
Some results and perspectives are presented in this paper.
Electric Energy Management in the Smart Home: Perspectives on Enabling Technologies and Consumer Behavior
- Adam Zipperer
- Patricia Aloise-Young
- Siddharth Suryanarayanan
- Robin Roche
- Lieko Earle
- Dane Christensen
- Pablo Bauleo
- Daniel Zimmerle
Zipperer, Adam, Patricia Aloise-Young, Siddharth Suryanarayanan, Robin Roche,
Lieko Earle, Dane Christensen, Pablo Bauleo, and Daniel Zimmerle. "Electric Energy Management in the Smart Home: Perspectives on Enabling Technologies and
Consumer Behavior." In Proceedings of the IEEE, pp. 2397-2408. IEEE, 2013.
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