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

Advancements in the Industrial Internet of Things for Energy Efficiency

The Internet of Things is an emerging field that leverages the connections of everyday objects for the betterment of society. A subfield of this trend, the Industrial Internet of Things (IIoT), has been referred to as an industrial revolution that enhances both productivity and safety in the industrial environment. While still in its early stages, identified improvements have the potential to markedly improve manufacturing productivity. Energy efficiency within manufacturing plants has traditionally received little focus. The Industrial Assessment Center Program demonstrates the potential energy improvements that can be realized in manufacturing plants, but these assessments also highlight some of the traditional barriers to energy efficiency. Some of these barriers include the lack of data to justify actionable improvements, unclear correlations between improvement costs and potential cost savings, and lack of knowledge on how energy improvements provide ancillary benefits to the plant. The IIoT has the potential to increase energy efficiency implementation in manufacturing plants by addressing these challenges. This dissertation discusses the framework in which energy efficiency enhancements within the IIoT environment can be realized. The dissertation initially details the potential benefits of IIoT for energy efficiency and presents a general framework for these improvements. While proposed IIoT frameworks vary, they all include the core elements of improved sensing capabilities, enhanced data analysis, and intelligent actuation. In addition to presenting the framework generally, the dissertation provides detailed case studies on how each of these framework elements lead to improved energy efficiency in manufacturing. The first case study demonstrates improved sensing capabilities in the IIoT framework. A non-intrusive flow meter for use in compressed air and other fluid systems is presented. The second case study discusses Autonomous Robotic Assessments of Energy, which use advanced data analysis to autonomously perform a lighting energy assessment in facilities. The third case study is then presented on intelligent actuation, which uses a novel k-means algorithm to autonomously determine appropriate times to actuate compressors for air systems in manufacturing plants. Each of the presented case studies includes experimental tests demonstrating their capabilities

Caracterización de la demanda de energía mediante patrones estocásticos en las Redes Eléctricas Inteligentes

[ES] La demanda de energía en todo el planeta continúa incrementándose de manera acele-rada. Por otro lado, la electrificación de diferentes sectores ha hecho que la demanda de electricidad crezca a una tasa aún mayor. Las redes eléctricas han evolucionado tecnológicamente en muchos aspectos, uno de ellos se refiere a la disponibilidad de datos de la demanda en diferentes puntos y niveles de la red, como en redes de trans-misión, redes de distribución y en los grandes y pequeños consumidores. Estos datos suministrados por las nuevas redes eléctricas inteligentes constituyen información de partida esencial para la gestión y planificación de los sistemas eléctricos. Los datos proporcionados por los medidores inteligentes de las redes eléctricas no tienen ninguna utilidad si no se analizan adecuadamente. A más del procesamiento adecuado de esos datos, se requieren herramientas que permitan obtener información útil. Los sistemas de gestión de la demanda de energía asociados al reconocimiento de patrones actualmente se han estudiado de manera escasa. En esta área de estudio se han identificado algunas limitaciones. Por ejemplo, la caracterización de la demanda de electricidad mediante el reconocimiento de patrones no se ha utilizado para la identifi-cación y valoración de cambios en el consumo de energía y los sistemas de monitori-zación no identifican posibles causas de las anomalías detectadas en la demanda de energía eléctrica. La predicción de la demanda es también una herramienta eficaz en la gestión de los sistemas de suministro eléctrico. Actualmente, herramientas tales como las redes neu-ronales y el aprendizaje profundo son las preferidas para realizar esta labor. Sin em-bargo presentan algunos inconvenientes, tales como, la dificultad para cuantificar la incertidumbre, requieren un gasto computacional elevado y esfuerzo considerable para establecer la estructura de la red neuronal que proporcione resultados adecuados. Con base en las limitaciones detectadas, en esta tesis se propone una nueva metodolo-gía estadística para caracterizar el comportamiento de la demanda de energía de los consumidores y otros puntos de la red eléctrica mediante la identificación y obtención de patrones. La utilización de estos patrones permite valorar e identificar cambios en perfiles de carga de electricidad. Además, la valoración de los cambios en la demanda eléctrica permite asociar estos valores a posibles eventos en una instalación. Esta me-todología puede ser empleada para detectar anomalías y catalogar perfiles de carga de acuerdo al cambio que han tenido con respecto a su comportamiento habitual, lo que permite identificar modos de trabajo de los sistemas eléctricos. En cuanto a la predic-ción de la demanda, se propone una metodología de simple aplicación para afrontar las limitaciones detectadas en las herramientas derivadas de la inteligencia artificial, de tal manera que sea posible acotar la incertidumbre de las predicciones realizadas. Esta información resulta útil en la gestión, ya que es posible generar alarmas, reducir costos en el mantenimiento y aplicar medidas adecuadas de eficiencia energética. Por otro lado, se propone un método para tratar los datos proporcionados por los medidores inteligentes, de tal manera que se ajusten a una distribución de probabilidad conocida, facilitando su interpretación y validando el análisis estadístico. Para ello, se ha rela-cionado a la potencia eléctrica activa absorbida por un consumidor o un sistema en general con la teoría existente de las series temporales de datos. Finalmente, se evalúa la aplicación del método y la metodología propuesta en diversos casos de estudio reales en diferentes puntos y niveles de la red eléctrica, identificando los beneficios que pueden obtenerse en la gestión de cada uno de ellos.[EN] The energy demand is increasing considerably every year worldwide. Moreover, the electrification of different sectors has caused the growth of electricity demand at an even higher rate. Thus, the electricity grids have technologically evolved in several aspects, one of them is the availability of demand data at different points and levels of the grid, such as in transmission and distribution systems, and large and small con-sumers. The data provided by the new smart grids are essential baseline information for the management and planning of electricity systems. The data provided by smart meters on electricity grids is worthless if it is not properly analysed. In addition to the appropriate processing of such data, tools are needed to obtain useful information. The energy demand management systems associated with pattern recognition have been scarcely studied at present. Some limitations have been identified in this area of study. For example, the characterization of electricity demand through pattern recognition has not been used to identify and evaluate changes in en-ergy consumption, whereas monitoring systems do not identify the possible causes of the anomalies detected in electricity demand. Demand forecasting is an effective tool in the management of electricity supply sys-tems. Currently, tools such as neural networks and deep learning are preferred for this purpose. However, they present remarkable drawbacks, such as the difficulty in quan-tifying uncertainties, the requirement of huge computational resources, and considera-ble effort to establish the structure of the neural network to provide adequate results. Based on the limitations detected, this thesis proposes a new statistical methodology to characterize the behaviour of the energy demand of consumers and other points of the electricity grid by identifying and obtaining patterns. The use of these patterns allows the assessment and identification of changes in electricity load profiles. Besides, the evaluation of changes in electricity demand allows these values to be associated with possible events in an installation. This methodology can be used to detect anomalies and to catalogue load profiles according to the changes they have had from their usual behaviour, which allows the identification of working modes of electrical systems. Concerning the prediction of demand, a simple application methodology is proposed to overcome the limitations detected in the instruments derived from artificial intelli-gence, which allows quantifying the uncertainty of the performed predictions. This information is useful for management since it is possible to generate alarms, reduce maintenance costs, and apply appropriate energy efficiency measures. On the other hand, a method is proposed for processing the data provided by the intelligent meters in order to adjust them to a known probability distribution, facilitating their interpre-tation and validating the statistical analysis. For this purpose, it has been related to the active electrical power absorbed by a consumer or a system in general with the exist-ing theory of time-series data. Finally, the application of the proposed methodologies is evaluated in several real case studies at different points and levels of the electricity grid, identifying the benefits that can be obtained in the management of each of these systems.[CA] La demanda d'energia a tot el planeta continua incrementant-se de manera accelerada. D'altra banda, l'electrificació de diferents sectors ha fet que la demanda d'electricitat creixi a una taxa encara més gran. Les xarxes elèctriques han evolucionat tecnològica-ment en molts aspectes, un d'ells es refereix a la disponibilitat de dades de la demanda en diferents punts i nivells de la xarxa, com en les de transmissió, de distribució i en els grans i petits consumidors. Aquestes dades subministrades per les noves xarxes elèctriques intel¿ligents constitueixen informació de partida essencial per a la gestió i planificació dels sistemes elèctrics. Les dades proporcionades pels mesuradors intel¿ligents de les xarxes elèctriques no tenen cap utilitat si no s'analitzen adequadament. A més del processament adequat d'aquestes dades, es requereixen eines que permetin obtenir informació útil. Els siste-mes de gestió de la demanda d'energia associats al reconeixement de patrons actual-ment s'han estudiat de manera escassa. En aquesta àrea d'estudi s'han identificat algunes limitacions. Per exemple, la caracterització de la demanda d'electricitat mitjançant el reconeixement de patrons no s'ha utilitzat per a la identificació i valoració de canvis en el consum d'energia i els sistemes de monitorització no identifiquen possibles causes de les anomalies detectades en la demanda d'energia elèctrica. La predicció de la demanda és també una eina eficaç en la gestió dels sistemes de sub-ministrament elèctric. Actualment, eines com ara les xarxes neuronals i l'aprenentatge profund són les preferides per a realitzar aquesta tasca. Però presenten alguns inconve-nients, com ara, la dificultat per quantificar la incertesa, requereixen una despesa computacional elevada i un esforç considerable per a establir l'estructura de la xarxa neuronal que proporcioni resultats adequats. Amb base en les limitacions detectades, en aquesta tesi es proposa una nova metodolo-gia estadística per caracteritzar el comportament de la demanda d'energia dels consu-midors i altres punts de la xarxa elèctrica mitjançant la identificació i obtenció de patrons. La utilització d'aquests patrons permet valorar i identificar canvis en perfils de càrrega d'electricitat. A més, la valoració dels canvis en la demanda elèctrica permet associar aquests valors a possibles esdeveniments en una instal¿lació. Aquesta metodo-logia pot ser emprada per detectar anomalies i catalogar perfils de càrrega d'acord al canvi que han tingut pel que fa al seu comportament habitual, el que permet identifi-car maneres de utilització dels sistemes elèctrics. En quant a la predicció de la deman-da, es proposa una metodologia de simple aplicació per afrontar les limitacions detec-tades en les eines derivades de la intel¿ligència artificial, de tal manera que sigui pos-sible delimitar la incertesa de les prediccions realitzades. Aquesta informació és útil en la gestió, ja que és possible generar alarmes, reduir costos en el manteniment i aplicar mesures adequades d'eficiència energètica. D'altra banda, es proposa un mètode per a tractar les dades proporcionades pels mesuradors intel¿ligents, de tal manera que s'ajus-tin a una distribució de probabilitat coneguda, facilitant la seva interpretació i validant l'anàlisi estadístic. Per a la qual cosa, s'ha relacionat a la potència elèctrica activa ab-sorbida per un consumidor o un sistema en general amb la teoria existent de les sèries temporals de dades. Finalment, s'avalua l'aplicació del mètode i la metodologia proposada en diversos casos d'estudi reals en diferents punts i nivells de la xarxa elèctrica, identificant els beneficis que es poden obtenir a la gestió de cada un d'ells.Serrano Guerrero, JX. (2020). Caracterización de la demanda de energía mediante patrones estocásticos en las Redes Eléctricas Inteligentes [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/153810TESI