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Characterization of Household Electricity Consumption in Uruguay
Abstract
This article presents a study to characterize the electricity consumption in residential buildings in Uruguay. Understanding residential electricity consumption is a relevant concept to identify factors that influence electricity usage, and allows developing specific and custom energy efficiency policies. The study focuses on two home appliances: air conditioner and water heater, which represents a large share of the electricity consumption of Uruguayan households. A data-analysis approach is applied to process several data sources and compute relevant indicators. Statistical methods are applied to study the relationships between different relevant variables, including appliance ownership, average income of households, and temperature, and the residential electricity consumption. A specific application of the data analysis is presented: a regression model to determine the consumption patterns of water heaters in households. Results show that the proposed approach is able to compute good values for precision, recall and F1-score and an excellent value for accuracy (0.92). These results are very promising for conducting an economic analysis that takes into account the investment cost of remotely controlling water heaters and the benefits derived from managing their demand.
... In developed countries, this may already be a reality; however, in developing countries, there is still a long way to go to achieve this goal [3]. In Latin America, the incorporation of the Internet of Things (IoT) in buildings and homes could be a challenge [4]. In Mexico, the majority of buildings are aging structures that lack the required infrastructure for the use of IoT or artificial intelligence (AI) technologies [5]. ...
The development of smart cities will require the construction of smart buildings. Smart buildings will demand the incorporation of elements for efficient monitoring and control of electrical consumption. The development of efficient AI algorithms is needed to generate more accurate electricity consumption predictions; therefore; anomaly detection in electricity consumption predictions has become an important research topic. This work focuses on the study of the detection of anomalies in domestic electrical consumption in Mexico. A predictive machine learning model of future electricity consumption was generated to evaluate various anomaly-detection techniques. Their effectiveness in identifying outliers was determined, and their performance was documented. A 30-day forecast of electrical consumption and an anomaly-detection model have been developed using isolation forest. Isolation forest successfully captured up to 75% of the anomalies. Finally, the Shapley values have been used to generate an explanation of the results of a model capable of detecting anomalous data for the Mexican context.
Building energy consumption prediction has a significant effect on energy control, design optimization, retrofit evaluation, energy price guidance, and prevention and control of COVID-19 in buildings, providing a guarantee for energy efficiency and carbon neutrality. This study reviews 116 research papers on data-driven building energy prediction from the perspective of data and machine learning algorithms and discusses feasible techniques for prediction across time scales, building levels, and energy consumption types in the context of the factors affecting data-driven building energy prediction. The review results revealed that the outdoor dry-bulb temperature is a vital factor affecting building energy consumption. In data-driven building energy consumption prediction, data preprocessing enables prediction across time scales, energy consumption feature extraction enables prediction across energy consumption types, and hyperparameter optimization enables prediction across time scales and building layers.
The recent global warming effect has brought into focus different solutions for combating climate change. The generation of climate-friendly renewable energy alternatives has been vastly improved and commercialized for power generation. As a result of this industrial revolution, solar photovoltaic (PV) systems have drawn much attention as a power generation source for varying applications, including the main utility-grid power supply. There has been tremendous growth in both on- and off-grid solar PV installations in the last few years. This trend is expected to continue over the next few years as government legislation and awareness campaigns increase to encourage a shift toward using renewable energy alternatives. Despite the numerous advantages of solar PV power generation, the highly variable nature of the sun’s irradiance in different seasons of various geopolitical areas/regions can significantly affect the expected energy yield. This variation directly impacts the profitability or economic viability of the system, and cannot be neglected. To overcome this challenge, various procedures have been applied to forecast the generated solar PV energy. This study provides a comprehensive and systematic review of recent advances in solar PV power forecasting techniques with a focus on data-driven procedures. It critically analyzes recent studies on solar PV power forecasting to highlight the strengths and weaknesses of the techniques or models implemented. The clarity provided will form a basis for higher accuracy in future models and applications.
This article introduces a dataset containing electricity consumption records of residential households in Uruguay (mostly in Montevideo). The dataset is conceived to analyze customer behavior and detect patterns of energy consumption that can help to improve the service. The dataset is conformed by three subsets that cover total household consumption, electric water heater consumption, and by-appliance electricity consumption, with sample intervals from one to fifteen minutes. The datetime ranges of the recorded consumptions vary depending on the subset, from some weeks long to some years long. The data was collected by the Uruguayan electricity company (UTE) and studied by Universidad de la República. The presented dataset is a valuable input for researchers in the study of energy consumption patterns, energy disaggregation, the design of energy billing plans, among other relevant issues related to the intelligent utilization of energy in modern smart cities.
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The methodology described in this article is applicable to design proper management strategies for demand response in smart electricity grids to fairly select water heaters to intervene while guaranteeing the lower discomfort of users.
Abstract
Demand-response techniques are crucial for providing a proper quality of service under the paradigm of smart electricity grids. However, control strategies may perturb and cause discomfort to clients. This article proposes a methodology for defining an index to estimate the discomfort associated with an active demand management consisting of the interruption of domestic electric water heaters. Methods are applied to build the index include pattern detection for estimating the water utilization using an Extra Trees ensemble learning method and a linear model for water temperature, both based on analysis of real data. In turn, Monte Carlo simulations are applied to calculate the defined index. The proposed approach is evaluated over one real scenario and two simulated scenarios to validate that the thermal discomfort index correctly models the impact on temperature. The simulated scenarios consider a number of households using water heaters to analyze and compare the thermal discomfort index for different interruptions and the effect of using different penalty terms for deviations of the comfort temperature. The obtained results allow designing a proper management strategy to fairly decide which water heaters should be interrupted to guarantee the lower discomfort of users.
Renewable energy is essential for planet sustainability. Renewable energy output forecasting has a significant impact on making decisions related to operating and managing power systems. Accurate prediction of renewable energy output is vital to ensure grid reliability and permanency and reduce the risk and cost of the energy market and systems. Deep learning's recent success in many applications has attracted researchers to this field and its promising potential is manifested in the richness of the proposed methods and the increasing number of publications. To facilitate further research and development in this area, this paper provides a review of deep learning-based solar and wind energy forecasting research published during the last five years discussing extensively the data and datasets used in the reviewed works, the data pre-processing methods, deterministic and probabilistic methods, and evaluation and comparison methods. The core characteristics of all the reviewed works are summarised in tabular forms to enable methodological comparisons. The current challenges in the field and future research directions are given. The trends show that hybrid forecasting models are the most used in this field followed by Recurrent Neural Network models including Long Short-Term Memory and Gated Recurrent Unit, and in the third place Convolutional Neural Networks. We also find that probabilistic and multistep ahead forecasting methods are gaining more attention. Moreover, we devise a broad taxonomy of the research using the key insights gained from this extensive review, the taxonomy we believe will be vital in understanding the cutting-edge and accelerating innovation in this field.
Home electricity demand has increased uninterrupted and is expected in 2050 to doubles the demanded in 2010. Making reasonable use of electricity is increasingly important and, in that way, different policies are carried out based on knowledge of how it is used. This article presents a procedure for measuring the potential electricity consumption in Uruguay. The study takes as main axis the appliance ownership information revelled by a national survey about severe socioeconomic aspects, and combines it with data on the characteristics of appliances, collected from local shops with an internet presence. Based on this data, an index of potential electricity consumption is performed for different census areas. To validate the analysis, it uses electricity consumption data from the ECD-UY (Electricity Consumption Data set of UruguaY) dataset and performs OLS linear regressions to evaluate real consumption and index correlation. The implementation uses Jupyter notebooks, language Python version 3, and utils libraries such as Pandas and Numpy. Results indicate that the departments with the highest index score are located on the West/Southwest coastlines. About census sections and segments in Montevideo, results show that the highest score areas are located in the South/Southeast coastlines, while lowest score ones are located in the outskirts. The validation process was limited by the lack of real consumption data.
This article presents an approach for the participation of datacenters and supercomputing facilities in smart electricity markets. This is a relevant problem in modern smart grid systems to implement demand response strategies for a better use of resources to guarantee energy efficiency. The proposed approach includes a datacenter model based on empirical information to determine the power consumption of CPU-intensive and memory-intensive tasks. A negotiation approach between the datacenter and its tenants and a heuristic planning method for energy reduction optimization are proposed. The experimental evaluation is performed over realistic problem instances modeling the operation of the National Supercomputing Center in Uruguay. The obtained results indicate that the proposed approach is effective to provide appropriate demand response actions according to monetary incentives. Accurate results are reported for realistic problem instances and different types of tenants.
Transportation systems play a major role in modern urban contexts, where citizens are expected to travel in order to engage in social and economic activities. Modern transportation systems incorporate technologies that generate huge volumes of data, which can be processed to extract valuable mobility information. This article describes a proposal for studying public transportation systems following an urban data analysis approach. A thorough analysis of the transportation system in Montevideo, Uruguay, and its usage is outlined, combining several sources of urban data. Furthermore, origin-destination matrices, which describe mobility patterns in the city, are generated using ticket sales data. The computed results are validated with a recent mobility survey. Finally, a visualization web application is presented, which allows conveying mobility information in an intuitive way.
This article presents electricity demand forecasting models for industrial and residential facilities, developed using ensemble machine learning strategies. Short term electricity demand forecasting is beneficial for both consumers and suppliers, as it allows improving energy efficiency policies and the rational use of resources. Computational intelligence models are developed for day-ahead electricity demand forecasting. An ensemble strategy is applied to build the day-ahead forecasting model based on several one-hour models. Three steps of data preprocessing are carried out, including treating missing values, removing outliers, and standardization. Feature extraction is performed to reduce overfitting, reducing the training time and improving the accuracy. The best model is optimized using grid search strategies on hyperparameter space. Then, an ensemble of 24 instances is generated to build the complete day-ahead forecasting model. Considering the computational complexity of the applied techniques, they are developed and evaluated on the National Supercomputing Center (Cluster-UY), Uruguay. Three different real data sets are used for evaluation: an industrial park in Burgos (Spain), the total electricity demand for Uruguay, and demand from a distribution substation in Montevideo (Uruguay). Standard performance metrics are applied to evaluate the proposed models. The main results indicate that the best day ahead model based on ExtraTreesRegressor has a mean absolute percentage error of 2:55% on industrial data, 5:17% on total consumption data and 9:09% on substation data.
Breaking down the aggregated energy consumption into a detailed consumption per appliance is a crucial tool for energy efficiency in residential buildings. Non-intrusive load monitoring allows implementing this strategy using just a smart energy meter without installing extra hardware. The obtained information is critical to provide an accurate characterization of energy consumption in order to avoid an overload of the electric system, and also to elaborate special tariffs to reduce the electricity cost for users. This article presents an approach for energy consumption disaggregation in households, based on detecting similar consumption patterns from previously recorded labelled datasets. The experimental evaluation of the proposed method is performed over four different problem instances that model real household scenarios using data from an energy consumption repository. Experimental results are compared with twobuilt-in algorithms provided by the nilmtk framework (combinatorial optimization and factorial hidden Markov model). The proposed algorithm was able to achieve accurate results regarding standard prediction metrics. The accuracy was not affected in a significant manner by the presence of ambiguity between the energy consumption of different appliances or by the difference of consumption between training and test appliances.
The increased penetration of photovoltaic (PV) generation introduces new challenges for the stability of electricity grids. In this work, machine learning (ML) techniques were implemented to forecast PV power production up to 1-hour ahead with a 10-minute granularity. Three different input combinations were utilised: Model 1 (M1) using the AC power only, Model 2 (M2) using the elevation angle (α), azimuth angle (φ) and AC power and Model 3 (M3) using α, φ, the AC power and satellite observations (SAT) aiming to improve the forecasting performance. Historical PV operational data were used for the training and validation stages of intra-hour PV forecasting models for time t + 10 to 60 minutes ahead. The results obtained over the test set period (15% of the data, i.e. ≈ 110 days) have shown that M2 exhibits the best-performance with a normalised root mean square error (nRMSE) in the range of 7.6% to 14.2%, whereas the skill score (SS) ranged between 6.5% and 30.9% for the 10-to 60-minute ahead, respectively. Index Terms-artificial neural networks, forecasting, PV power.
With the increasing use of large-scale grid-connected photovoltaic system, accurate forecast approach for the power output of photovoltaic system has become an important issue. In order to forecast the power output of a photovoltaic system at 24-hour-ahead without any complex modeling and complicated calculation, an artificial neural network based approach is proposed in this paper. The improved back-propagation learning algorithm is adopted to overcome shortcomings of the standard back-propagation learning algorithm. Similar day selection algorithm based on forecast day information is proposed to improve forecast accuracy in different weather types. Forecasting results of a photovoltaic system show that the proposed approach has a great accuracy and efficiency for forecasting the power output of photovoltaic system.
PV systems have been increasingly installed worldwide in recent years. Because it produces clean energy, moreover the development of technology is continued therefore the reliability is increasing and the price is decreasing in opposite. To implement the PV system, however, a significant limitation of PV system is the uncertainty of power from the sun. This will affect the quality of the electrical system that connected. Therefore, this article will present the power forecasting of a PV system by calculating the solar radiation, collecting data from weather forecasting, and using Elman neural network to forecast by using data from PV system installed at roof top of Faculty Science and Technology Rajamangala University of Technology Thanyaburi.The results of study found that the tendency to apply this method any further
This article presents an approach applying computational intelligence for detecting the use of air conditioners in households. The main objective is determining the intensive use of air conditioning with a high level of confidence. Unsupervised (-means) and supervised (Artificial Neural Networks) approaches are developed for classifying consumers for a case study in Uruguay, using data collected by a smart meters network and open weather data. Data from 29 Uruguayan cities were considered in the period from January 1, 2022, to December 31, 2022. Two thermal models are developed for estimating the temperature inside households. The main results indicate that the proposed approach is able to reach a high classification accuracy, up to 94.5% and a high classification recall, up to 95%, for the considered real case study. The final scope of the work is developing smart tools for classifying consumers, to design and suggest specific commercial products that promote energy efficiency.
In this paper, we propose a methodology based on machine learning techniques to characterize the flexibility of electricity consumption in the residential sector. The main challenge is that the characterization of flexibility requires to know which and when appliances are being used and how available are users to change its utilization: However, this type of data is not generally available. In this work, we propose a full-stack methodology to solve this problem: we start by processing total electricity consumption data with feature engineering; then we use Non-Intrusive Load Monitoring (NILM) or Interactive Learning (IL) to identify the use of the appliances with higher flexibility (water heating, space heating and clothes drier); then we apply a Random Forest classifier to identify when the flexible appliances are being used; and finally we apply a K-means clustering algorithm to evaluate the flexibility of such appliance. We compare the results using accuracy, recall, and f-score indicators. The results show that the proposed approach can be used to characterize with high accuracy the use of flexible appliances just based on aggregated electricity consumption collected by smart meters with a low sampling rate. Further, we also demonstrate that Interactive Learning is a viable alternative approach to NILM to disaggregate electricity consumption.
This article presents the evaluation of multicriteria planning heuristics for demand response in datacenters and supercomputing facilities. This is a relevant problem for science nowadays, when the growing application of cutting-edge technologies (numerical methods, big data processing, artificial intelligence, smart systems, etc.) has raised the energy demands in datacenters. The proposed approach involves a negotiation mechanism for colocation datacenters, where the datacenter operator agrees prices and quality of service with a group of tenants. Twelve different multicriteria heuristics are proposed for planning using both local and global information at tenants and datacenter operator levels. The proposed approach is evaluated applying simulations over realistic scenarios considering different tenant sizes and heterogeneity levels that model different business models for datacenters. Several metrics are computed and Pareto analysis is provided. The main results indicate that accurate trade-off values between the problem objectives are obtained, offering different options for decision making. The proposed approach provides a useful and applicable method for demand response planning in modern datacenters.
Energy demand management is an important technique for smart grids, under the paradigm of smart cities. Direct control of devices is useful for demand management, but it has the disadvantage of affecting user comfort. This article presents an approach for defining an index to estimate the discomfort associated with an active demand management consisting of the interruption of domestic electric water heaters to perform a load shifting. The index is defined based on estimations of water utilization and water temperature using continuous power consumption measurements of water heaters. A stochastic forecasting model is applied, including an Extra Trees Regressor and a linear model for water temperature. Monte Carlo simulations are performed to calculate the defined index. The evaluation of the proposed approach is performed using real data for both the forecasting model and the temperature model. The real effect of interruptions on the water temperature of two water heaters is compared to validate that the thermal discomfort index correctly models the impact on temperature. This result allows ordering devices by their thermal discomfort index and having a fair criterion to decide which ones should be interrupted.
This article presents the application of computational intelligence techniques for the characterization of electricity consumption in households. A specific variant of the residential energy disaggregation problem is solved, which proposes identifying the state changes in a electrical network, using a time series of aggregate household power consumption data. This article introduces and compares three classifiers to solve the problem: a Naive Bayes classifier, a K Nearest Neighbors algorithm, and a Long Short Term Memory neural network. The implemented classifiers are evaluated using the UK-DALE data repository. Experimental results show that the Long Short Term Memory network is the most accurate to deal with the characterization problem, achieving a successful rate of state changes up to 75% and values of F1-score close to 1.0 on certain appliances.
Alternative energy sources are becoming more and more common around the world. In order to reduce environmental pollution and CO 2 emissions, in addition to being an ideal solution to overcome the energy crisis. In this context, power energy stands out, as it is the most abundant and most widely available natural resource on the entire planet. Due to the high level of uncertainty of the factors that directly interfere in the generation of solar power, such as temperature and solar radiation, make predictions of solar power with high precision is a challenge. Thus, the objective of this article is to develop a forecasting model, through time series, that makes it possible to predict the production of power energy, using a database collected in a photovoltaic plant in Uruguay. For the development of the proposal, models (base-learners), pre-processing techniques and models (meta-learners) used in the Stacking-Ensemble Learnig (STACK) method were used, which were compared using the measurements of performance Relative Root Mean Square Error (RRMSE), Symmetric Mean Absolute Percentage Error (sMAPE) and Determination Coefficient (R 2 ) in addition to statistical tests. In the end, it can be concluded that the combination Correlation Matrix (CORR) and Language Model (LM), from Layer-0 obtained the best results, in the three performance measures and the combination of models (base-learners) and pre-processing techniques (Layer-0) presented the best results when compared to Layer-1, obtaining satisfactory values in all performance measures.
The outputs of photovoltaic (PV) power are random and uncertain due to the variations of meteorological elements, which may disturb the safety and stability of power system operation. Hence, precise day-ahead PV power forecasting is crucial in renewable energy utilization, as it is beneficial to power generation schedule and short-term dispatch of the PV integrated power grid. In this study, a novel day-ahead PV power forecasting approach based on deep learning is proposed and validated. Firstly, two novel deep convolutional neural networks (CNNs), i.e. residual network (ResNet) and dense convolutional network (DenseNet), are introduced as the core models of forecasting. Secondly, a new data preprocessing is proposed to construct input feature maps for the two novel CNNs, which involves historical PV power series, meteorological elements and numerical weather prediction. Thirdly, a meta learning strategy based on multi-loss-function network is proposed to train the two deep networks, which can ensure a high robustness of the extracted convolutional features. Owing to the learning strategy and unique architectures of the two novel CNNs, they are designed into relatively deep architectures with superb nonlinear representation abilities, which consist of more than ten layers. Both point and probabilistic forecasting results are provided in the case study, demonstrating the accuracy and reliability of the proposed forecasting approach.
Integration of photovoltaics into power grids is difficult as solar energy is highly dependent on climate and geography; often fluctuating erratically. This causes penetrations and voltage surges, system instability, inefficient utilities planning and financial loss. Forecast models can help; however, time stamp, forecast horizon, input correlation analysis, data pre and post-processing, weather classification, network optimization, uncertainty quantification and performance evaluations need consideration. Thus, contemporary forecasting techniques are reviewed and evaluated. Input correlational analyses reveal that solar irradiance is most correlated with Photovoltaic output, and so, weather classification and cloud motion study are crucial. Moreover, the best data cleansing processes: normalization and wavelet transforms, and augmentation using generative adversarial network are recommended for network training and forecasting. Furthermore, optimization of inputs and network parameters, using genetic algorithm and particle swarm optimization, is emphasized. Next, established performance evaluation metrics MAE, RMSE and MAPE are discussed, with suggestions for including economic utility metrics. Subsequently, modelling approaches are critiqued, objectively compared and categorized into physical, statistical, artificial intelligence, ensemble and hybrid approaches. It is determined that ensembles of artificial neural networks are best for forecasting short term photovoltaic power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty. Additionally, convolutional neural network is found to excel in eliciting a model's deep underlying non-linear input-output relationships. The conclusions drawn impart fresh insights in photovoltaic power forecast initiatives, especially in the use of hybrid artificial neural networks and evolutionary algorithms.
Forecasting the day-ahead electricity load is beneficial for both suppliers and consumers. The reduction of electricity waste and the rational dispatch of electric generator units can be significantly improved with accurate load forecasts. This article is focused on studying and developing computational intelligence techniques for electricity load forecasting. Several models are developed to forecast the electricity load of the next hour using real data from an industrial pole in Spain. Feature selection and feature extraction are performed to reduce overfitting and therefore achieve better models, reducing the training time of the developed methods. The best of the implemented models is optimized using grid search strategies on hyperparameter space. Then, twenty four different instances of the optimal model are trained to forecast the next twenty four hours. Considering the computational complexity of the applied techniques, they are developed and evaluated on the computational platform of the National Supercomputing Center (Cluster-UY), Uruguay. Standard performance metrics are applied to evaluate the proposed models. The main results indicate that the best model based on ExtraTreesRegressor obtained has a mean absolute percentage error of 2.55% on day ahead hourly forecast which is a promising result.
This article describes the national initiative for installing and operating a collaborative scientific HPC infrastructure in Uruguay (Cluster-UY). The project was conceived as a mean to foster research and innovation projects that face complex problems with high computing demands. The main ideas and motivations of the Cluster-UY project are described. The technological decisions to install the platform are explained and the collaborative operation model to guarantee sustainability is introduced. In addition, the perspectives of the national scientific HPC initiative are highlighted and sample current projects are presented.
Power forecasting is an important factor for planning the operations of photovoltaic (PV) system. This paper presents an advanced statistical method for solar power forecasting based on artificial intelligence techniques. The method requires as input past power measurements and meteorological forecasts of solar irradiance, relative humidity and temperature at the site of the photovoltaic power system. A self-organized map (SOM) is trained to classify the local weather type of 24 h ahead provided by the online meteorological services. A unique feature of the method is that following a preliminary weather type classification, the neural networks can be well trained to improve the forecast accuracy. The proposed method is suitable for operational planning of transmission system operator, i.e. forecasting horizon of 24 h ahead and for PV power system operators trading in electricity markets. Application of the forecasting method on the power production of an actual PV power system shows the validity of the method.
We evaluate and compare several forecasting techniques using no exogenous inputs for predicting the solar power output of a 1 MWp, single-axis tracking, photovoltaic power plant operating in Merced, California. The production data used in this work corresponds to hourly averaged power collected from November 2009 to August 2011. Data prior to January 2011 is used to train the several forecasting models for the 1 and 2 h-ahead hourly averaged power output. The methods studied in this work are: Persistent model, Auto-Regressive Integrated Moving Average (ARIMA), k-Nearest-Neighbors (kNNs), Artificial Neural Networks (ANNs), and ANNs optimized by Genetic Algorithms (GAs/ANN). The accuracy of the models is determined by computing error statistics such as mean absolute error (MAE), mean bias error (MBE), and the coefficient of correlation (R2) for the differences between the forecasted values and the measured values for the period from January to August of 2011. This work also addresses the accuracy of the different methods as a function of the variability of the power output, which depends strongly on seasonal conditions. The findings show that the ANN-based forecasting models perform better than the other forecasting techniques, that substantial improvements can be achieved with a GA optimization of the ANN parameters, and that the accuracy of all models depends strongly on seasonal characteristics of solar variability.