Non-intrusive load monitoring under residential solar power influx

This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method for a consumer premises with a residentially installed solar plant. This method simultaneously identifies the amount of solar power influx as well as the turned ON appliances, their operating modes, and power consumption levels. Further, it works effectively with a single active power measurement taken at the total power entry point with a sampling rate of 1 Hz. First, a unique set of appliance and solar signatures were constructed using a high-resolution implementation of Karhunen Loéve expansion (KLE). Then, different operating modes of multi-state appliances were automatically classified utilizing a spectral clustering based method. Finally, using the total power demand profile, through a subspace component power level matching algorithm, the turned ON appliances along with their operating modes and power levels as well as the solar influx amount were found at each time point. The proposed NILM method was first successfully validated on six synthetically generated houses (with solar units) using real household data taken from the Reference Energy Disaggregation Dataset (REDD) - USA. Then, in order to demonstrate the scalability of the proposed NILM method, it was employed on a set of 400 individual households. From that, reliable estimations were obtained for the total residential solar generation and for the total load that can be shed to provide reserve services. Finally, through a developed prediction technique, NILM results observed from 400 households during four days in the recent past were utilized to predict the next day’s total load that can be shed

Evolutionary multivariate time series prediction

Multivariate time series (MTS) prediction plays a significant role in many practical data mining applications, such as finance, energy supply, and medical care domains. Over the years, various prediction models have been developed to obtain robust and accurate prediction. However, this is not an easy task by considering a variety of key challenges. First, not all channels (each channel represents one time series) are informative (channel selection). Considering the complexity of each selected time series, it is difficult to predefine a time window used for inputs. Second, since the selected time series may come from cross domains collected with different devices, they may require different feature extraction techniques by considering suitable parameters to extract meaningful features (feature extraction), which influences the selection and configuration of the predictor, i.e., prediction (configuration). The challenge arising from channel selection, feature extraction, and prediction (configuration) is to perform them jointly to improve prediction performance. Third, we resort to ensemble learning to solve the MTS prediction problem composed of the previously mentioned operations,  where the challenge is to obtain a set of models satisfied both accurate and diversity. Each of these challenges leads to an NP-hard combinatorial optimization problem, which is impossible to be solved using the traditional methods since it is non-differentiable. Evolutionary algorithm (EA), as an efficient metaheuristic stochastic search technique, which is highly competent to solve complex combinatorial optimization problems having mixed types of decision variables, may provide an effective way to address the challenges arising from MTS prediction. The main contributions are supported by the following investigations. First, we propose a discrete evolutionary model, which mainly focuses on seeking the influential subset of channels of MTS and the optimal time windows for each of the selected channels for the MTS prediction task. A comprehensively experimental study on a real-world electricity consumption data with auxiliary environmental factors demonstrates the efficiency and effectiveness of the proposed method in searching for the informative time series and respective time windows and parameters in a predictor in comparison to the result obtained through enumeration. Subsequently, we define the basic MTS prediction pipeline containing channel selection, feature extraction, and prediction (configuration). To perform these key operations, we propose an evolutionary model construction (EMC) framework to seek the optimal subset of channels of MTS, suitable feature extraction methods and respective time windows applied to the selected channels, and parameter settings in the predictor simultaneously for the best prediction performance. To implement EMC, a two-step EA is proposed, where the first step EA mainly focuses on channel selection while in the second step, a specially designed EA works on feature extraction and prediction (configuration). A real-world electricity data with exogenous environmental information is used and the whole dataset is split into another two datasets according to holiday and nonholiday events. The performance of EMC is demonstrated on all three datasets in comparison to hybrid models and some existing methods. Then, based on the prediction pipeline defined previously, we propose an evolutionary multi-objective ensemble learning model (EMOEL) by employing multi-objective evolutionary algorithm (MOEA) subjected to two conflicting objectives, i.e., accuracy and model diversity. MOEA leads to a pareto front (PF) composed of non-dominated optimal solutions, where each of them represents the optimal subset of the selected channels, the selected feature extraction methods and the selected time windows, and the selected parameters in the predictor. To boost ultimate prediction accuracy, the models with respect to these optimal solutions are linearly combined with combination coefficients being optimized via a single-objective task-oriented EA. The superiority of EMOEL is identified on electricity consumption data with climate information in comparison to several state-of-the-art models. We also propose a multi-resolution selective ensemble learning model, where multiple resolutions are constructed from the minimal granularity using statistics. At the current time stamp, the preceding time series data is sampled at different time intervals (i.e., resolutions) to constitute the time windows. For each resolution, multiple base learners with different parameters are first trained. Feature selection technique is applied to search for the optimal set of trained base learners and least square regression is used to combine them. The performance of the proposed ensemble model is verified on the electricity consumption data for the next-step and next-day prediction. Finally, based on EMOEL and multi-resolution, instead of only combining the models generated from each PF, we propose an evolutionary ensemble learning (EEL) framework, where multiple PFs are aggregated to produce a composite PF (CPF) after removing the same solutions in PFs and being sorted into different levels of non-dominated fronts (NDFs). Feature selection techniques are applied to exploit the optimal subset of models in level-accumulated NDF and least square is used to combine the selected models. The performance of EEL that chooses three different predictors as base learners is evaluated by the comprehensive analysis of the parameter sensitivity. The superiority of EEL is demonstrated in comparison to the best result from single-objective EA and the best individual from the PF, and several state-of-the-art models across electricity consumption and air quality datasets, both of which use the environmental factors from other domains as the auxiliary factors. In summary, this thesis provides studies on how to build efficient and effective models for MTS prediction. The built frameworks investigate the influential factors, consider the pipeline composed of channel selection, feature extraction, and prediction (configuration) simultaneously, and keep good generalization and accuracy across different applications. The proposed algorithms to implement the frameworks use techniques from evolutionary computation (single-objective EA and MOEA), machine learning and data mining areas. We believe that this research provides a significant step towards constructing robust and accurate models for solving MTS prediction problems. In addition, with the case study on electricity consumption prediction, it will contribute to helping decision-makers in determining the trend of future energy consumption for scheduling and planning of the operations of the energy supply system

Predicción a corto plazo de Ia demanda horaria de energía eléctrica en España mediante modelos optimizados de Holt-Winters múltiple estacionales

[ES] La desregulación del sector de la electricidad producido por la Ley 54/1997 del Sector Eléctrico provocó un cambio radical en el sistema de gestión de la electricidad, tanto para los productores y distribuidores, como para los propios consumidores. España lideraba un cambio en la política energética buscando una liberalización del mercado en aplicación de la Directiva 96/92/CE del Parlamento Europeo y del Consejo. En este cambio, el Estado abandona la noción de servicio público para el sistema eléctrico y pasa a gestionarse mediante un mercado mayorista operado por sociedades mercantiles. Este cambio se traduce en que la gestión del sistema se realiza mediante un sistema de mercados de oferta y de demanda, y que el Estado únicamente establecerá la regulación pertinente. Dentro del mismo cambio, se organiza el sistema de modo que aparece el transportista único del sistema, Red Eléctrica de España. Originalmente, este ente debe asegurar el suministro y realizar la panificación de la operativa del sistema, pero con la Ley 17/2007 de la adaptación del sector eléctrico se convierte en el transportista único del sistema. La Ley 24/2013, de 26 de diciembre, del Sector Eléctrico además le atribuye aún mayor responsabilidad, siendo el único operador del transporte y del sistema, adquiriendo la necesidad de realizar previsiones de demanda eléctrica que serán utilizadas en el mercado eléctrico, y, además, de precios de la energía. Estas previsiones se realizan habitualmente mediante la utilización de técnicas de series temporales, utilizando modelos de regresión, ARIMA, redes neuronales o de suavizado exponencial. Debido a que la energía eléctrica no es fácilmente acumulable, la producción debe estar ajustada a la demanda. Cualquier desfase entre ambas provoca costes enormes que las empresas del sector eléctrico necesitan evitar. Para ello, demandan predicciones del consumo lo más acertadas y fiables posibles. Esta tesis se centra en el estudio de los modelos de Holt-Winters para ser utilizados en la previsión de demanda eléctrica en España. Estos modelos han demostrado ser sencillos de trabajar y robustos frente a variaciones no controladas y han sido adaptados para trabajar con múltiples estacionalidades. Con ello se han desarrollado nuevos modelos que han permitido mejorar las previsiones. En primer lugar, se estudia la demanda eléctrica en España, como eje fundamental para el desarrollo de la tesis. Se observa cómo la serie dispone de una características muy relevante: una frecuencia de 24 horas, con una media y varianza que no son constantes. Se observa la presencia de varias estacionalidades que se integran en el modelo, así como una enorme influencia de los días festivos y fines de semana. Por último, se detecta una alta volatilidad. Este análisis permite conocer el comportamiento de la serie e introducir los modelos múltiple-estacionales. En segundo lugar, se presentan y analizan los modelos de Holt-Winters múltiple-estacionales, como eje vertebrador de la tesis. Estos modelos son los desarrollados en la tesis para conseguir sus objetivos: se presentan los modelos, se analizan los valores iniciales y la optimización de los parámetros, y finalmente se analizan los parámetros. Finalmente se introducen nuevos elementos en los modelos que permiten mejorar las previsiones realizadas por los mismos. En este aspecto, se incluye la introducción de estacionalidades discretas que permiten modelizar los días festivos; se introducen indicadores turísticos que mejora la previsión en las zonas cuyo producto interior bruto depende altamente del turismo; finalmente, se introduce un modelo híbrido en el que las condiciones climáticas son consideradas y que aumenta la precisión de las previsiones. Por último, esta tesis viene acompañada de un desarrollo de software específico para la explotación del modelo, desarrollado como Toolbox de MATLAB®. En definitiva, se desarr[CA] La desregulació del sector de l'electricitat produït per la Llei 54/1997, del sector elèctric va provocar un canvi radical en el sistema de gestió de l'electricitat, tant per als productors i distribuïdors, com per als propis consumidors. Espanya liderava un canvi en la política energètica buscant una liberalització del mercat aplicant la Directiva 96/92/CE del Parlament Europeu i del Consell. En aquest canvi, l'Estat abandona la noció de servei públic per al sistema elèctric i passa a gestionar-se mitjançant un mercat majorista operat per societats mercantils. Aquest canvi es tradueix en que la gestió del sistema es realitza mitjançant un sistema de mercats d'oferta i de demanda, i que l'Estat únicament ha d'establir la regulació pertinent. Dins el mateix canvi, s'organitza el sistema de manera que apareix el transportista únic del sistema, Red Eléctrica de España. Originalment, aquest ens ha d'assegurar el subministrament i realitzar la panificació de l'operativa del sistema, però amb la Llei 17/2007 de l'adaptació del sector elèctric es converteix en el transportista únic del sistema. La Llei 24/2013, de 26 de desembre, del sector elèctric a més li atribueix a REE ser l'operador únic del transport i del sistema, adquirint encara més gran responsabilitat i la necessitat de realitzar previsions de demanda elèctrica que seran utilitzades en el mercat elèctric, i, a més, de preus de l'energia. Aquestes previsions es fan habitualment mitjançant la utilització de tècniques de sèries temporals, utilitzant models de regressió, ARIMA, xarxes neuronals o de suavitzat exponencial. A causa de que l'energia elèctrica no és fàcilment acumulable, la producció ha d'estar ajustada a la demanda. Qualsevol desfasament entre les dues provoca costos enormes que les empreses del sector elèctric necessiten evitar. Per a això, demanen prediccions del consum el més encertades i fiables possibles. Aquesta tesi se centra en l'estudi dels models de Holt-Winters per ser utilitzats en la previsió de demanda elèctrica a Espanya. Aquests models han demostrat ser senzills de treballar i robustos davant de variacions no controlades i han estat adaptats per treballar amb múltiples estacionalitats. Amb això s'han desenvolupat nous models que han permès millorar les previsions. En primer lloc, s'estudia la demanda elèctrica a Espanya, com a eix fonamental per al desenvolupament de la tesi. S'observa com la sèrie disposa de característiques molt rellevants: una freqüència de 24 hores, amb una mitjana i variància que no són constants. S'observa la presència de diverses estacionalitats que s'integren en el model, així com una enorme influència dels dies festius i caps de setmana. Finalment, es detecta una alta volatilitat. Aquesta anàlisi permet conèixer el comportament de la sèrie i introduir els models múltiple estacionals. En segon lloc, es presenten i s'analitzen els models de Holt-Winters múltiple estacionals, com a eix vertebrador de la tesi. Aquests models són els desenvolupats en la tesi per aconseguir els seus objectius: es presenten els models, s'analitzen els valors inicials i l'optimització dels paràmetres, i finalment s'analitzen els paràmetres. Finalment s'introdueixen nous elements en els models que permeten millorar les previsions realitzades pels mateixos. En aquest aspecte, s'inclou la introducció de estacionalitats discretes que permeten modelitzar els dies festius; s'introdueixen indicadors turístics que millora la previsió en les zones el producte interior brut depèn altament del turisme; finalment, s'introdueix un model híbrid en el qual les condicions climàtiques són considerades i que augmenta la precisió de les previsions. Addicionalment, aquesta tesi ve acompanyada d'un desenvolupament de programari específic per a l'explotació del model, desenvolupat com Toolbox de Matlab®. En definitiva, es desenvolupen i implanten nous models de Holt-Winters que pro[EN] The deregulation of the electricity sector produced by Law 54/1997 of the Electricity Sector caused a radical change in the electricity management system, both for producers and distributors, and for the consumers themselves. Spain was leading a change in energy policy seeking a liberalization of the market by applying Directive 96/92/EC of the European Parliament and the Council. In this change, the State abandons the notion of public service for the electrical system and it is managed through a wholesale market operated by mercantile companies. This change means that the management of the system is carried out through a system of supply and demand markets, and that the State will only establish the relevant regulation. Within the same change, the system is organized so that the single transporter of the system, Red Eléctrica de España, appears. Originally, this entity must ensure the supply and carry out the baking of the operation of the system, but with the law 17/2007 of the adaptation of the electricity sector becomes the only carrier of the system. Law 24/2013, of December 26, of the Electricity Sector also gives it even greater responsibility, acquiring the need to make forecasts of electric demand that will be used in the electricity market, and, in addition, of energy prices. These forecasts are usually made through the use of time series techniques, using regression models, ARIMA, neural networks or exponential smoothing. Because electric power is not easily accumulated, production must be adjusted to the demand. Any gap between the two causes huge costs that companies in the electricity sector need to avoid. For this, they demand predictions of consumption as accurate and reliable as possible. This thesis focuses on the study of Holt-Winters models to be used in forecasting electricity demand in Spain. These models have proven to be simple to work and robust against uncontrolled variations and have been adapted to work with multiple seasons. This new models have been developed that have improved forecasts. In the first place, the electrical demand in Spain is studied, as a fundamental axis for the development of the thesis. It is observed how the series has very relevant characteristics: a frequency of 24 hours, with a mean and variance that are not constant. It is observed the presence of several seasons that are integrated into the model, as well as a huge influence of holidays and weekends. Finally, high volatility is detected. This analysis allows to know the behavior of the series and introduce the multiple seasonal models. Secondly, seasonal multiple Holt-Winters models are presented and analyzed as the backbone of the thesis. These models are those developed in the thesis to achieve their objectives: the models are presented, the initial values and the optimization of the parameters are analyzed, and finally the parameters are analyzed. Finally, new elements are introduced in the models that allow improving the forecasts made by them. In this aspect, the introduction of discrete seasonings that allow modeling holidays is included; Tourist indicators are introduced that improve forecasting in areas whose gross domestic product depends highly on tourism; finally, a hybrid model is introduced in which the climatic conditions are considered and which increases the accuracy of the forecasts. Additionally, this thesis is accompanied by a development of specific software for the exploitation of the model, developed as MATLAB® Toolbox. In short, new models of Holt-Winters are developed and implemented that provide more accurate short-term forecasts, which allow the entities that form the electrical system to better plan and manage the electrical system.Trull Domínguez, Ó. (2020). Predicción a corto plazo de Ia demanda horaria de energía eléctrica en España mediante modelos optimizados de Holt-Winters múltiple estacionales [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/140091TESI