Autonomous cycle of data analysis tasks for scheduling the use of controllable load appliances using renewable energy

International Conference on Computational Science and Computational Intelligence, 15/12/2021-17/12/2021, Estados Unidos.With the arrival of smart edifications with renewable energy generation capacities, new possibilities for optimizing the use of the energy network appear. In particular, this work defines a system that automatically generates hours of use of the controllable load appliances (washing machine, dishwasher, etc.) within these edifications, in such a way that the use of renewable energy is maximized. To achieve this, we are based on the hypothesis that depending on the climate, a prediction can be made of how much energy will be generated and, according to the behavior of the users, the energy demand required by these appliances. Following this hypothesis, we build an autonomous cycle of data analysis tasks composed of three tasks, two tasks for estimating the required load (demand) and the renewable energy produced (supply), coupled with a scheduling task to generate the plans of use of appliances. The results indicate that it is possible to carry out optimal scheduling of the use of appliances, but that they depend on the quality of the predictions of supply and demand.European CommissionAgencia Estatal de InvestigaciónJunta de Comunidades de Castilla-La Manch

IEEE Access Special Section Editorial: Energy Management in Buildings

Energy usage in buildings has become a critical concern globally, and with that, the concept of energy management in buildings has emerged to help tackle these challenges. The energy management system provides a new opportunity for the building's energy requirements, and is an essential method for energy service, i.e., energy saving, consumption,

Smart home appliances scheduling to manage energy usage

Abstract: It is imperative to manage household appliances in a cost-effective way to realize efficient energy utilization, reduce spending on electricity bills and increase grid reliability. This study presents a Home energy management system (HEMS) scheduling analysis. The scheduling plan avoids the electricity wastages which arise majorly due resident’s negligence on appliances control. The home appliances employed in the research work are classified in terms of their operating periods. The energy consumption was evaluated using a Fixed Pricing (FP) data. The appliances scheduling plan was developed using Microsoft.net framework with C# Programming language whereas the front end showing the scheduled operating periods for the appliances was developed using Telerik UI framework for Windows forms. Simulation results of the scheduling plan show energy consumption in homes can be planned, monitored and controlled to avoid energy wastage and minimize energy expenditure

Optimized energy consumption model for smart home using improved differential evolution algorithm

Abstract: This paper proposes an improved enhanced differential evolution algorithm for implementing demand response between aggregator and consumer. The proposed algorithm utilizes a secondary population archive, which contains unfit solutions that are discarded by the primary archive of the earlier proposed enhanced differential evolution algorithm. The secondary archive initializes, mutates and recombines candidates in order to improve their fitness and then passes them back to the primary archive for possible selection. The capability of this proposed algorithm is confirmed by comparing its performance with three other wellperforming evolutionary algorithms: enhanced differential evolution, multiobjective evolutionary algorithm based on dominance and decomposition, and non-dominated sorting genetic algorithm III. This is achieved by testing the algorithms’ ability to optimize a multiobjective optimization problem representing a smart home with demand response aggregator. Shiftable and non-shiftable loads are considered for the smart home which model energy usage profile for a typical household in Johannesburg, South Africa. In this study, renewable sources include battery bank and rooftop photovoltaic panels. Simulation results show that the proposed algorithm is able to optimize energy usage by balancing load scheduling and contribution of renewable sources, while maximizing user comfort and minimizing peak-to-average ratio

Internet of things (IoT) based adaptive energy management system for smart homes

PhD ThesisInternet of things enhances the flexibility of measurements under different environments, the development of advanced wireless sensors and communication networks on the smart grid infrastructure would be essential for energy efficiency systems. It makes deployment of a smart home concept easy and realistic. The smart home concept allows residents to control, monitor and manage their energy consumption with minimal wastage. The scheduling of energy usage enables forecasting techniques to be essential for smart homes. This thesis presents a self-learning home management system based on machine learning techniques and energy management system for smart homes. Home energy management system, demand side management system, supply side management system, and power notification system are the major components of the proposed self-learning home management system. The proposed system has various functions including price forecasting, price clustering, power forecasting alert, power consumption alert, and smart energy theft system to enhance the capabilities of the self-learning home management system. These functions were developed and implemented through the use of computational and machine learning technologies. In order to validate the proposed system, real-time power consumption data were collected from a Singapore smart home and a realistic experimental case study was carried out. The case study had proven that the developed system performing well and increased energy awareness to the residents. This proposed system also showcases its customizable ability according to different types of environments as compared to traditional smart home models. Forecasting systems for the electricity market generation have become one of the foremost research topics in the power industry. It is essential to have a forecasting system that can accurately predict electricity generation for planning and operation in the electricity market. This thesis also proposed a novel system called multi prediction system and it is developed based on long short term memory and gated recurrent unit models. This proposed system is able to predict the electricity market generation with high accuracy. Multi Prediction System is based on four stages which include a data collecting and pre-processing module, a multi-input feature model, multi forecast model and mean absolute percentage error. The data collecting and pre-processing module preprocess the real-time data using a window method. Multi-input feature model uses single input feeding method, double input feeding method and multiple feeding method for features input to the multi forecast model. Multi forecast model integrates long short term memory and gated recurrent unit variations such as regression model, regression with time steps model, memory between batches model and stacked model to predict the future generation of electricity. The mean absolute percentage error calculation was utilized to evaluate the accuracy of the prediction. The proposed system achieved high accuracy results to demonstrate its performance

A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles

[EN] Electricity consumption patterns reveal energy demand behaviors and enable strategY implementation to increase efficiency using monitoring systems. However, incorrect patterns can be obtained when the time-series components of electricity demand are not considered. Hence, this research proposes a new method for handling time-series components that significantly improves the ability to obtain patterns and detect anomalies in electrical consumption profiles. Patterns are found using the proposed method and two widespread methods for handling the time-series components, in order to compare the results. Through this study, the conditions that electricity demand data must meet for making the time-series analysis useful are established. Finally, one year of real electricity consumption is analyzed for two different cases to evaluate the effect of time-series treatment in the detection of anomalies. The proposed method differentiates between periods of high or low energy demand, identifying contextual anomalies. The results indicate that it is possible to reduce time and effort involved in data analysis, and improve the reliability of monitoring systems, without adding complex procedures.Serrano-Guerrero, X.; Escrivá-Escrivá, G.; Luna-Romero, S.; Clairand, J. (2020). A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles. Energies. 13(5):1-23. https://doi.org/10.3390/en13051046S123135Hong, T., Yang, L., Hill, D., & Feng, W. (2014). Data and analytics to inform energy retrofit of high performance buildings. Applied Energy, 126, 90-106. doi:10.1016/j.apenergy.2014.03.052Ogunjuyigbe, A. S. O., Ayodele, T. R., & Akinola, O. A. (2017). User satisfaction-induced demand side load management in residential buildings with user budget constraint. Applied Energy, 187, 352-366. doi:10.1016/j.apenergy.2016.11.071Huang, Y., Sun, Y., & Yi, S. (2018). Static and Dynamic Networking of Smart Meters Based on the Characteristics of the Electricity Usage Information. Energies, 11(6), 1532. doi:10.3390/en11061532Lin, R., Ye, Z., & Zhao, Y. (2019). OPEC: Daily Load Data Analysis Based on Optimized Evolutionary Clustering. Energies, 12(14), 2668. doi:10.3390/en12142668Hunt, L. C., Judge, G., & Ninomiya, Y. (2003). Underlying trends and seasonality in UK energy demand: a sectoral analysis. Energy Economics, 25(1), 93-118. doi:10.1016/s0140-9883(02)00072-5Serrano-Guerrero, X., Escrivá-Escrivá, G., & Roldán-Blay, C. (2018). Statistical methodology to assess changes in the electrical consumption profile of buildings. Energy and Buildings, 164, 99-108. doi:10.1016/j.enbuild.2017.12.059Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection. ACM Computing Surveys, 41(3), 1-58. doi:10.1145/1541880.1541882Escrivá-Escrivá, G., Álvarez-Bel, C., Roldán-Blay, C., & Alcázar-Ortega, M. (2011). New artificial neural network prediction method for electrical consumption forecasting based on building end-uses. Energy and Buildings, 43(11), 3112-3119. doi:10.1016/j.enbuild.2011.08.008Serrano-Guerrero, X., Prieto-Galarza, R., Huilcatanda, E., Cabrera-Zeas, J., & Escriva-Escriva, G. (2017). Election of variables and short-term forecasting of electricity demand based on backpropagation artificial neural networks. 2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). doi:10.1109/ropec.2017.8261630Jain, R. K., Smith, K. M., Culligan, P. J., & Taylor, J. E. (2014). Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Applied Energy, 123, 168-178. doi:10.1016/j.apenergy.2014.02.057Singh, S., & Yassine, A. (2018). Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting. Energies, 11(2), 452. doi:10.3390/en11020452Jota, P. R. S., Silva, V. R. B., & Jota, F. G. (2011). Building load management using cluster and statistical analyses. International Journal of Electrical Power & Energy Systems, 33(8), 1498-1505. doi:10.1016/j.ijepes.2011.06.034Shareef, H., Ahmed, M. S., Mohamed, A., & Al Hassan, E. (2018). Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers. IEEE Access, 6, 24498-24509. doi:10.1109/access.2018.2831917Crespo Cuaresma, J., Hlouskova, J., Kossmeier, S., & Obersteiner, M. (2004). Forecasting electricity spot-prices using linear univariate time-series models. Applied Energy, 77(1), 87-106. doi:10.1016/s0306-2619(03)00096-5Janczura, J., Trück, S., Weron, R., & Wolff, R. C. (2013). Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling. Energy Economics, 38, 96-110. doi:10.1016/j.eneco.2013.03.013Angelos, E. W. S., Saavedra, O. R., Cortés, O. A. C., & de Souza, A. N. (2011). Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems. IEEE Transactions on Power Delivery, 26(4), 2436-2442. doi:10.1109/tpwrd.2011.2161621Milton, M.-A., Pedro, C.-O., Xavier, S.-G., & Guillermo, E.-E. (2018). Characterization and Classification of Daily Electricity Consumption Profiles: Shape Factors and k-Means Clustering Technique. E3S Web of Conferences, 64, 08004. doi:10.1051/e3sconf/20186408004Chicco, G. (2012). Overview and performance assessment of the clustering methods for electrical load pattern grouping. Energy, 42(1), 68-80. doi:10.1016/j.energy.2011.12.031Seem, J. E. (2005). Pattern recognition algorithm for determining days of the week with similar energy consumption profiles. Energy and Buildings, 37(2), 127-139. doi:10.1016/j.enbuild.2004.04.004Seem, J. E. (2007). Using intelligent data analysis to detect abnormal energy consumption in buildings. Energy and Buildings, 39(1), 52-58. doi:10.1016/j.enbuild.2006.03.033Li, X., Bowers, C. P., & Schnier, T. (2010). Classification of Energy Consumption in Buildings With Outlier Detection. IEEE Transactions on Industrial Electronics, 57(11), 3639-3644. doi:10.1109/tie.2009.2027926Capozzoli, A., Piscitelli, M. S., Brandi, S., Grassi, D., & Chicco, G. (2018). Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings. Energy, 157, 336-352. doi:10.1016/j.energy.2018.05.127Jokar, P., Arianpoo, N., & Leung, V. C. M. (2016). Electricity Theft Detection in AMI Using Customers’ Consumption Patterns. IEEE Transactions on Smart Grid, 7(1), 216-226. doi:10.1109/tsg.2015.2425222Fenza, G., Gallo, M., & Loia, V. (2019). Drift-Aware Methodology for Anomaly Detection in Smart Grid. IEEE Access, 7, 9645-9657. doi:10.1109/access.2019.2891315Araya, D. B., Grolinger, K., ElYamany, H. F., Capretz, M. A. M., & Bitsuamlak, G. (2017). An ensemble learning framework for anomaly detection in building energy consumption. Energy and Buildings, 144, 191-206. doi:10.1016/j.enbuild.2017.02.058Hayes, M. A., & Capretz, M. A. (2015). Contextual anomaly detection framework for big sensor data. Journal of Big Data, 2(1). doi:10.1186/s40537-014-0011-yCui, W., & Wang, H. (2017). A New Anomaly Detection System for School Electricity Consumption Data. Information, 8(4), 151. doi:10.3390/info8040151Fan, C., Xiao, F., Zhao, Y., & Wang, J. (2018). Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data. Applied Energy, 211, 1123-1135. doi:10.1016/j.apenergy.2017.12.005Cai, H., Shen, S., Lin, Q., Li, X., & Xiao, H. (2019). Predicting the Energy Consumption of Residential Buildings for Regional Electricity Supply-Side and Demand-Side Management. IEEE Access, 7, 30386-30397. doi:10.1109/access.2019.2901257Khan, I., Huang, J. Z., Masud, M. A., & Jiang, Q. (2016). Segmentation of Factories on Electricity Consumption Behaviors Using Load Profile Data. IEEE Access, 4, 8394-8406. doi:10.1109/access.2016.2619898Al-Jarrah, O. Y., Al-Hammadi, Y., Yoo, P. D., & Muhaidat, S. (2017). Multi-Layered Clustering for Power Consumption Profiling in Smart Grids. IEEE Access, 5, 18459-18468. doi:10.1109/access.2017.2712258Park, K.-J., & Son, S.-Y. (2019). A Novel Load Image Profile-Based Electricity Load Clustering Methodology. IEEE Access, 7, 59048-59058. doi:10.1109/access.2019.2914216Serrano-Guerrero, X., Siavichay, L.-F., Clairand, J.-M., & Escrivá-Escrivá, G. (2019). Forecasting Building Electric Consumption Patterns Through Statistical Methods. Advances in Emerging Trends and Technologies, 164-175. doi:10.1007/978-3-030-32033-1_16Li, Y., Zhang, H., Liang, X., & Huang, B. (2019). Event-Triggered-Based Distributed Cooperative Energy Management for Multienergy Systems. IEEE Transactions on Industrial Informatics, 15(4), 2008-2022. doi:10.1109/tii.2018.2862436Khalid, A., Javaid, N., Guizani, M., Alhussein, M., Aurangzeb, K., & Ilahi, M. (2018). Towards Dynamic Coordination Among Home Appliances Using Multi-Objective Energy Optimization for Demand Side Management in Smart Buildings. IEEE Access, 6, 19509-19529. doi:10.1109/access.2018.2791546Borovkova, S., & Geman, H. (2006). Analysis and Modelling of Electricity Futures Prices. Studies in Nonlinear Dynamics & Econometrics, 10(3). doi:10.2202/1558-3708.137

Demand response for smart homes

RÉSUMÉ: Problèmes dans l’opération de la transmission d’électricité, surcharge, émission de carbone sont, entre autres, les préoccupations des gestionnaires de réseaux électriques partout dans le monde. Dans ce contexte, face au besoin de réduire les coûts d’exploitation ainsi que le besoin d’adaptation aux différentes exigences de qualité, de sécurité, de flexibilité et de durabilité, les réseaux intelligents sont considérés comme une révolution technologique dans le secteur de l’énergie électrique. Cette transformation sera nécessaire pour atteindre les objectifs environnementaux, intégrer la participation de la demande, appuyer l’adoption de véhicules électriques et hybrides ainsi que la production distribuée à basse tension. Chaque partie prenante dans le processus de gestion de l’énergie peut avoir des avantages avec le réseau intelligent, ce qui justifie son importance dans l’actualité. Dans ce travail, on se concentre plutôt sur l’utilisateur final. En plus de l’utilisateur final, nous utilisons également l’agrégateur, qui est une entité qui agrège un ensemble d’utilisateurs de sorte que l’union de leurs participations individuelles devienne plus représentative pour les décisions relatives au système d’énergie. La fonction de l’agrégateur est d’établir un engagement d’intérêts entre les utilisateurs finaux et l’entreprise de génération afin de satisfaire les deux parties. L’une des contributions principales de cette thèse est la mise au point d’une méthode qui donne à un agrégateur la possibilité de coordonner la consommation d’un ensemble d’utilisateurs, en maintenant le niveau de confort souhaité pour chacun d’entre eux et en les encourageant via des incitations monétaires à changer ses consommations, de sorte que la charge globale ait le coût minimal pour le producteur. Dans la première contribution (chapitre 4), ce travail se concentre sur le développement d’un modèle mathématique représentatif pour la planification des équipements d’un utilisateur. Le modèle intègre des modèles détaillés et fiables pour des équipements spécifiques tout en conservant une complexité telle que les solveurs commerciaux puissent résoudre le problème en quelques secondes. Notre modèle peut donner des résultats qui, comparés aux modèles les plus proches de la littérature, permettent des économies de coûts allant de 8% à 389% sur un horizon de 24 heures. Dans la deuxième contribution (chapitre 5), l’accent a été mis sur la création d’un cadre algorithimique destiné à aider un utilisateur final particulier dans son processus de décision lié à la récupération d’investissement sur l’acquisition d’appareils ou d’équipements (composants) intelligents. Pour un utilisateur spécifique, le cadre analyse différentes combinaisons de composants intelligents afin de déterminer lequel est le plus rentable et à quel moment il convient de l’installer. Ce cadre peut être utilisé pour encourager un utilisateur à adopter un concept de maison intelligente réduisant les risques liés à son investissement. La troisième contribution(chapitre 6) regroupe plusieurs maisons intelligentes. Un cadre algorithimique basé sur les programmes de réponse à la demande est proposé. Il utilise les résultats des deux contributions précédentes pour représenter plusieurs utilisateurs, et son objectif est de maximiser le bien-être social, en tenant compte de la réduction des coûts pour un producteur donné ainsi que de la satisfaction de chaque consommateur. Les résultats montrent que, du point de vue du producteur, la courbe de charge globale est aplatie sans que cela ait un impact négatif sur le confort des utilisateurs ou sur leurs coûts. Enfin, les expériences rapportées dans chaque contribution valident théoriquement l’efficacité des approches proposées.----------ABSTRACT: Transmission operation issues, overload, carbon emissions are, among others, the concerns of power system operators worldwide. In this context, faced with the need to reduce operating costs and the need to adapt to the different requirements of quality, security, flexibility and sustainability, smart grids are seen as a technological revolution in the field of power system. This transformation will be necessary to achieve environmental objectives, support the adoption of electric and hybrid vehicles, improve distributed low-voltage generation and integrate demand participation. Each stakeholder in the energy management process can have advantages with the smart grid, which justifies its current importance. The focus of this thesis is rather on the end user. In addition to the end-user, this work also uses the aggregator that is an entity that aggregates a set of users such that the union of the individual participation of each user becomes more representative for power system decisions. The function of the aggregator is to establish an engagement of interests between the end users and the generator company in order to satisfy both parties. One of the main contributions of this thesis is the development of a method that gives an aggregator the possibility to coordinate the consumption of a set of users, keeping the desired comfort level for each of them and encouraging them via monetary incentives to change their consumption such that their aggregated load has the minimal cost for the generator company. In the first contribution (Chapter 4), this work focuses on developing a representative mathematical model for user appliances scheduling. The model integrates detailed and reliable models for specific appliances while keeping a complexity such that commercial solvers are able to solve the problem in seconds. Our model can give results that, compared to the closest models in the literature, provide a cost savings in the range of 8% and 389% over a scheduling horizon of 24 hours. In the second contribution (Chapter 5), the focus was given in making a framework to help a specific end-user in their decision process related to the payback for an acquisition of smart appliances or equipment (components). For a specific user, the framework analyses various combinations of smart components to discover which one is the most profitable and when it should be installed. This framework can be used to encourage users towards a smart home concept decreasing the risks about their investment. The third contribution (Chapter 6) aggregates several smart homes. A framework based on demand response programs is proposed. It uses outputs from the two previous contributions to represent multiple users, and its goal is to maximize the social welfare, considering the reduction of costs for a given generator company as well the satisfaction of every user. Results show that, from the generator company perspective, the aggregate load consumption is flattened without impacting negatively the users’ comfort or their costs. Finally, the experiments reported in each contribution validate, in theory, the efficiency of the proposed approaches

Deep Learning in Demand Side Management: A Comprehensive Framework for Smart Homes

The advent of deep learning has elevated machine intelligence to an unprecedented high level. Fundamental concepts, algorithms, and implementations of differentiable programming, including gradient-based measures such as gradient descent and backpropagation, have powered many deep learning algorithms to accomplish millions of tasks in computer vision, signal processing, natural language comprehension, and recommender systems. Demand-side management (DSM) serves as a crucial tactic on the customer side of meters which regulates electricity consumption without hampering the occupant comfort of homeowners. As more residents participate in the energy management program, DSM will further contribute to grid stability protection, economical operation, and carbon emission reduction. However, DSM cannot be implemented effectively without the penetration of smart home technologies that integrate intelligent algorithms into hardware. Resident behaviors being analyzed and comprehended by deep learning algorithms based on sensor-collected human activities data is one typical example of such technology integration. This thesis applies deep learning to DSM and provides a comprehensive framework for smart home management. Firstly, a detailed literature review is conducted on DSM, smart homes, and deep learning. Secondly, the four papers published during the candidate’s Ph.D. career are utilized in lieu of thesis chapters: “A Demand-Side Load Event Detection Algorithm Based on Wide-Deep Neural Networks and Randomized Sparse Backpropagation,” “A Novel High-Performance Deep Learning Framework for Load Recognition: Deep-Shallow Model Based on Fast Backpropagation,” “An Object Surveillance Algorithm Based on Batch-Normalized CNN and Data Augmentation in Smart Home,” “Integrated optimization algorithm: A metaheuristic approach for complicated optimization.” Thirdly, a discussion section is offered to synthesize ideas and key results of the four papers published. Conclusion and directions for future research are provided in the final section of this thesis