A novel ensemble method for electric vehicle power consumption forecasting: Application to the Spanish system

The use of electric vehicle across the world has become one of the most challenging issues for environmental policies. The galloping climate change and the expected running out of fossil fuels turns the use of such non-polluting cars into a priority for most developed countries. However, such a use has led to major concerns to power companies, since they must adapt their generation to a new scenario, in which electric vehicles will dramatically modify the curve of generation. In this paper, a novel approach based on ensemble learning is proposed. In particular, ARIMA, GARCH and PSF algorithms' performances are used to forecast the electric vehicle power consumption in Spain. It is worth noting that the studied time series of consumption is non-stationary and adds difficulties to the forecasting process. Thus, an ensemble is proposed by dynamically weighting all algorithms over time. The proposal presented has been implemented for a real case, in particular, at the Spanish Control Centre for the Electric Vehicle. The performance of the approach is assessed by means of WAPE, showing robust and promising results for this research field.Ministerio de Economía y Competitividad Proyectos ENE2016-77650-R, PCIN-2015-04 y TIN2017-88209-C2-R

A novel soft computing approach based on FIR to model and predict energy dynamic systems

Tesi en modalitat compendi de publicacionsWe are facing a global climate crisis that is demanding a change in the status quo of how we produce, distribute and consume energy. In the last decades, this is being redefined through Smart Grids(SG), an intelligent electrical network more observable, controllable, automated, fully integrated with energy services and the end-users. Most of the features and proposed SG scenarios are based on reliable, robust and fast energy predictions. For instance, for proper planning activities, such as generation, purchasing, maintenance and investment; for demand side management, like demand response programs; for energy trading, especially at local level, where productions and consumptions are more stochastics and dynamic; better forecasts also increase grid stability and thus supply security. A large variety of Artificial Intelligence(AI) techniques have been applied in the field of Short-term electricity Load Forecasting(SLF) at consumer level in low-voltage system, showing a better performance than classical techniques. Inaccuracy or failure in the SLF process may be translated not just in a non-optimal (low prediction accuracy) solution but also in frustration of end-users, especially in new services and functionalities that empower citizens. In this regard, some limitations have been observed in energy forecasting models based on AI such as robustness, reliability, accuracy and computation in the edge. This research proposes and develops a new version of Fuzzy Inductive Reasoning(FIR), called Flexible FIR, to model and predict the electricity consumption of an entity in the low-voltage grid with high uncertainties, and information missing, as well as the capacity to be deployed either in the cloud or locally in a new version of Smart Meters(SMs) based on Edge Computing(EC). FIR has been proved to be a powerful approach for model identification and system ’s prediction over dynamic and complex processes in different real world domains but not yet in the energy domain. Thus, the main goal of this thesis is to demonstrate that a new version of FIR, more robust, reliable and accurate can be a referent Soft Computing(SC) methodology to model and predict dynamic systems in the energy domain and that it is scalable to an EC integration. The core developments of Flexible FIR have been an algorithm that can cope with missing information in the input values, as well as learn from instances with Missing Values(MVs) in the knowledge-based, without compromising significantly the accuracy of the predictions. Moreover, Flexible FIR comes with new forecasting strategies that can cope better with loss of causality of a variable and dispersion of output classes than classical k nearest neighbours, making the FIR forecasting process more reliable and robust. Furthermore, Flexible FIR addresses another major challenge modelling with SC techniques, which is to select best model parameters. One of the most important parameters in FIR is the number k of nearest neighbours to be used in the forecast process. The challenge to select the optimal k, dynamically, is addressed through an algorithm, called KOS(K nearest neighbour Optimal Selection), which has been developed and tested also with real world data. It computes a membership aggregation function of all the neighbours with respect their belonging to the output classes.While with KOS the optimal parameter k is found online, with other approaches such as genetic algorithms or reinforcement learning is not, which increases the computational time.Ens trobem davant una crisis climàtica global que exigeix un canvi al status quo de la manera que produïm, distribuïm i consumim energia. En les darreres dècades, està sent redefinit gràcies a les xarxa elèctriques intel·ligents(SG: Smart Grid) amb millor observabilitat, control, automatització, integrades amb nous serveis energètics i usuaris finals. La majoria de les funcionalitats i escenaris de les SG es basen en prediccions de la càrrega elèctrica confiables, robustes i ràpides. Per les prediccions de càrregues elèctriques a curt termini(SLF: Short-term electricity Load Forecasting), a nivell de consumidors al baix voltatge, s’han aplicat una gran varietat de tècniques intel·ligència Artificial(IA) mostrant millor rendiment que tècniques estadístiques tradicionals. Un baix rendiment en SLF, pot traduir-se no només en una solució no-òptima (baixa precisió de predicció) sinó també en la frustració dels usuaris finals, especialment en nous serveis i funcionalitats que empoderarien als ciutadans. En el marc d’aquesta investigació es proposa i desenvolupa una nova versió de la metodologia del Raonament Inductiu Difús(FIR: Fuzzy Inductive Reasoning), anomenat Flexible FIR, capaç de modelar i predir el consum d’electricitat d’una entitat amb un grau d’incertesa molt elevat, inclús amb importants carències d’informació (missing values). A més, Flexible FIR té la capacitat de desplegar-se al núvol, així como localment, en el que podria ser una nova versió de Smart Meters (SM) basada en tecnologia d’Edge Computing (EC). FIR ja ha demostrat ser una metodologia molt potent per la generació de models i prediccions en processos dinàmics en diferents àmbits, però encara no en el de l’energia. Per tant, l’objectiu principal d’aquesta tesis és demostrar que una versió millorada de FIR, més robusta, fiable i precisa pot consolidar-se com una metodologia Soft Computing SC) de referencia per modelar i predir sistemes dinàmics en aplicacions per al sector de l’energia i que és escalable a una integració d’EC. Les principals millores de Flexible FIR han estat, en primer lloc, el desenvolupament i test d’un algorisme capaç de processar els valors d’entrada d’un model FIR tot i que continguin Missing Values (MV). Addicionalment, aquest algorisme també permet aprendre d’instàncies amb MV en la matriu de coneixement d’un model FIR, sense comprometre de manera significativa la precisió de les prediccions. En segon lloc, s’han desenvolupat i testat noves estratègies per a la fase de predicció, comportant-se millor que els clàssics k veïns més propers quan ens trobem amb pèrdua de causalitat d’una variable i dispersió en les classes de sortida, aconseguint un procés d’aprenentatge i predicció més confiable i robust. En tercer lloc, Flexible FIR aborda un repte molt comú en tècniques de SC: l’òptima parametrització del model. En FIR, un dels paràmetres més determinants és el número k de veïns més propers que s’utilitzaran durant la fase de predicció. La selecció del millor valor de k es planteja de manera dinàmica a través de l’algorisme KOS (K nearest neighbour Optimal Selection) que s’ha desenvolupat i testat també amb dades reals. Mentre que amb KOS el paràmetre òptim de k es calcula online, altres enfocaments mitjançant algoritmes genètics o aprenentatge per reforç el càlcul és offline, incrementant significativament el temps de resposta, sent a més a més difícil la implantació en escenaris d’EC. Aquestes millores fan que Flexible FIR es pugui adaptar molt bé en aplicacions d’EC. En aquest sentit es proposa el concepte d’un SM de segona generació basat en EC, que integra Flexible FIR com mòdul de predicció d’electricitat executant-se en el propi dispositiu i un agent EC amb capacitat per el trading d'energia produïda localment. Aquest agent executa un innovador mecanisme basat en incentius, anomenat NRG-X-Change que utilitza una nova moneda digital descentralitzada per l’intercanvi d’energia, que s’anomena NRGcoin.Estamos ante una crisis climática global que exige un cambio del status quo de la manera que producimos, distribuimos y consumimos energía. En las últimas décadas, este status quo está siendo redefinido debido a: la penetración de las energías renovables y la generación distribuida; nuevas tecnologías como baterías y paneles solares con altos rendimientos; y la forma en que se consume la energía, por ejemplo, a través de vehículos eléctricos o con la electrificación de los hogares. Estas palancas requieren una red eléctrica inteligente (SG: Smart Grid) con mayor observabilidad, control, automatización y que esté totalmente integrada con nuevos servicios energéticos, así como con sus usuarios finales. La mayoría de las funcionalidades y escenarios de las redes eléctricas inteligentes se basan en predicciones de la energía confiables, robustas y rápidas. Por ejemplo, para actividades de planificación como la generación, compra, mantenimiento e inversión; para la gestión de la demanda, como los programas de demand response; en el trading de electricidad, especialmente a nivel local, donde las producciones y los consumos son más estocásticos y dinámicos; una mejor predicción eléctrica también aumenta la estabilidad de la red y, por lo tanto, mejora la seguridad. Para las predicciones eléctricas a corto plazo (SLF: Short-term electricity Load Forecasting), a nivel de consumidores en el bajo voltaje, se han aplicado una gran variedad de técnicas de Inteligencia Artificial (IA) mostrando mejor rendimiento que técnicas estadísticas convencionales. Un bajo rendimiento en los modelos predictivos, puede traducirse no solamente en una solución no-óptima (baja precisión de predicción) sino también en frustración de los usuarios finales, especialmente en nuevos servicios y funcionalidades que empoderan a los ciudadanos. En este sentido, se han identificado limitaciones en modelos de predicción de energía basados en IA, como la robustez, fiabilidad, precisión i computación en el borde. En el marco de esta investigación se propone y desarrolla una nueva versión de la metodología de Razonamiento Inductivo Difuso (FIR: Fuzzy Inductive Reasoning), que hemos llamado Flexible FIR, capaz de modelar y predecir el consumo de electricidad de una entidad con altos grados de incertidumbre e incluso con importantes carencias de información (missing values). Además, Flexible FIR tiene la capacidad de desplegarse en la nube, así como localmente, en lo que podría ser una nueva versión de Smart Meters (SM) basada en tecnología de Edge Computing (EC). En el pasado, ya se ha demostrado que FIR es una metodología muy potente para la generación de modelos y predicciones en procesos dinámicos, sin embargo, todavía no ha sido demostrado en el campo de la energía. Por tanto, el objetivo principal de esta tesis es demostrar que una versión mejorada de FIR, más robusta, fiable y precisa puede consolidarse como metodología Soft Computing (SC) de referencia para modelar y predecir sistemas dinámicos en aplicaciones para el sector de la energía y que es escalable hacia una integración de EC. Las principales mejoras en Flexible FIR han sido, en primer lugar, el desarrollo y testeo de un algoritmo capaz de procesar los valores de entrada en un modelo FIR a pesar de que contengan Missing Values (MV). Además, dicho algoritmo también permite aprender de instancias con MV en la matriz de conocimiento de un modelo FIR, sin comprometer de manera significativa la precisión de las predicciones. En segundo lugar, se han desarrollado y testeado nuevas estrategias para la fase de predicción de un modelo FIR, comportándose mejor que los clásicos k vecinos más cercanos ante la pérdida de causalidad de una variable y dispersión de clases de salida, consiguiendo un proceso de aprendizaje y predicción más confiable y robusto. En tercer lugar, Flexible FIR aborda un desafío muy común en técnicas de SC: la óptima parametrización del modelo. En FIR, uno de los parámetros más determinantes es el número k de vecinos más cercanos que se utilizarán en la fase de predicción. La selección del mejor valor de k se plantea de manera dinámica a través del algoritmo KOS (K nearest neighbour Optimal Selection) que se ha desarrollado y probado también con datos reales. Dicho algoritmo calcula una función de membresía agregada, de todos los vecinos, con respecto a su pertenencia a las clases de salida. Mientras que con KOS el parámetro óptimo de k se calcula online, otros enfoques mediante algoritmos genéticos o aprendizaje por refuerzo, el cálculo es offline incrementando significativamente el tiempo de respuesta, siendo además difícil su implantación en escenarios de EC. Estas mejoras hacen que Flexible FIR se adapte muy bien en aplicaciones de EC, en las que la analítica de datos en streaming debe ser fiable, robusta y con un modelo suficientemente ligero para ser ejecutado en un IoT Gateway o dispositivos más pequeños. También, en escenarios con poca conectividad donde el uso de la computación en la nube es limitado y los parámetros del modelo se calculan localmente. Con estas premisas, en esta tesis, se propone el concepto de un SM de segunda generación basado en EC, que integra Flexible FIR como módulo de predicción de electricidad ejecutándose en el dispositivo y un agente EC con capacidad para el trading de energía producida localmente. Dicho agente ejecuta un novedoso mecanismo basado en incentivos, llamado NRG-X-Change que utiliza una nueva moneda digital descentralizada para el intercambio de energía, llamada NRGcoin.Postprint (published version

Use of Sensors and Analyzers Data for Load Forecasting: A Two Stage Approach

This article belongs to the Special Issue Sensors for Smart GridsThe increase in sensors in buildings and home automation bring potential information to improve buildings' energy management. One promissory field is load forecasting, where the inclusion of other sensors' data in addition to load consumption may improve the forecasting results. However, an adequate selection of sensor parameters to use as input to the load forecasting should be done. In this paper, a methodology is proposed that includes a two-stage approach to improve the use of sensor data for a specific building. As an innovation, in the first stage, the relevant sensor data is selected for each specific building, while in the second stage, the load forecast is updated according to the actual forecast error. When a certain error is reached, the forecasting algorithm (Artificial Neural Network or K-Nearest Neighbors) is trained with the most recent data instead of training the algorithm every time. Data collection is provided by a prototype of agent-based sensors developed by the authors in order to support the proposed methodology. In this case study, data over a period of six months with five-minute time intervals regarding eight types of sensors are used. These data have been adapted from an office building to illustrate the advantages of the proposed methodology.This work has received funding from Portugal 2020 under SPEAR project (NORTE-01-0247-FEDER-040224), in the scope of ITEA 3 SPEAR Project 16001 and from FEDER Funds through COMPETE program and from National Funds through (FCT) under the project UIDB/00760/2020, and CEECIND/02887/2017info:eu-repo/semantics/publishedVersio

Identification and characterization of irregular consumptions of load data

The historical information of loadings on substation helps in evaluation of size of photovoltaic (PV) generation and energy storages for peak shaving and distribution system upgrade deferral. A method, based on consumption data, is proposed to separate the unusual consumption and to form the clusters of similar regular consumption. The method does optimal partition of the load pattern data into core points and border points, high and less dense regions, respectively. The local outlier factor, which does not require fixed probability distribution of data and statistical measures, ranks the unusual consumptions on only the border points, which are a few percent of the complete data. The suggested method finds the optimal or close to optimal number of clusters of similar shape of load patterns to detect regular peak and valley load demands on different days. Furthermore, identification and characterization of features pertaining to unusual consumptions in load pattern data have been done on border points only. The effectiveness of the proposed method and characterization is tested on two practical distribution systems

Distribution Level Building Load Prediction Using Deep Learning

Load prediction in distribution grids is an important means to improve energy supply scheduling, reduce the production cost, and support emission reduction. Determining accurate load predictions has become more crucial than ever as electrical load patterns are becoming increasingly complicated due to the versatility of the load profiles, the heterogeneity of individual load consumptions, and the variability of consumer-owned energy resources. However, despite the increase of smart grids technologies and energy conservation research, many challenges remain for accurate load prediction using existing methods. This dissertation investigates how to improve the accuracy of load predictions at the distribution level using artificial intelligence (AI), and in particular deep learning (DL), which have already shown significant progress in various other disciplines. Existing research that applies the DL for load predictions has shown improved performance compared to traditional models. The current research using conventional DL tends to be modeled based on the developer\u27s knowledge. However, there is little evidence that researchers have yet addressed the issue of optimizing the DL parameters using evolutionary computations to find more accurate predictions. Additionally, there are still questions about hybridizing different DL methods, conducting parallel computation techniques, and investigating them on complex smart buildings. In addition, there are still questions about disaggregating the net metered load data into load and behind-the-meter generation associated with solar and electric vehicles (EV). The focus of this dissertation is to improve the distribution level load predictions using DL. Five approaches are investigated in this dissertation to find more accurate load predictions. The first approach investigates the prediction performance of different DL methods applied for energy consumption in buildings using univariate time series datasets, where their numerical results show the effectiveness of recursive artificial neural networks (RNN). The second approach studies optimizing time window lags and network\u27s hidden neurons of an RNN method, which is the Long Short-Term Memory, using the Genetic Algorithms, to find more accurate energy consumption forecasting in buildings using univariate time series datasets. The third approach considers multivariate time series and operational parameters of practical data to train a hybrid DL model. The fourth approach investigates parallel computing and big data analysis of different practical buildings at the DU campus to improve energy forecasting accuracies. Lastly, a hybrid DL model is used to disaggregate residential building load and behind-the-meter energy loads, including solar and EV

Energy demand forecasting in smart buildings

Energy demand forecasting has become a relevant subject in the energy management field. Different techniques are being currently applied to forecast the energy demand for different time horizons and for diverse types of loads. Some of them are based in complex Machine Learning (ML) algorithms, which maps the energy consumption to a set of influence parameters or inputs, such as the historical data consumption, the weather or other variables, making it possible to predict the energy demand. Important management decisions from different stakeholders in the Energy sector are based on these predictions and, therefore, it is important to rigorously assess the performance of these predictive models. A specific methodology is presented in this dissertation through its application over a real-building case-study in which energy demand predictions are being carried out by a ML model. All the steps in the evaluation process are explained and exemplified, including the data gathering, evaluation period selection, data preprocess with special emphasis in the data abnormalities an its relation to the process dynamics and, finally, the data process itself. The accuracy of the model and the main parameters of influence are evaluated through four different metrics and data visualizations, based mainly in box-andwhisker plots. Several anomalies when predicting energy consumption in a disaggregated load (single building) have been found in the study. By removing them the stability of the case-study model is around 88%. The metrics yield a MAPE (Mean Absolute Percentage Error) of 18.05% and a MBPE (Mean Biased Percentage Error) of -4.67%. While being values within the literature range they show a poor accuracy. Nevertheless, there is space for improvement and by retraining, refining and calibrating the model it will be possible to improve its performance. The day of the week, the working calendar and the hour of the day showed to have a strong influence over the error metrics analyzed. Other alernative Machine Learnings methodologies have been applied to the same dataset and their performance have been analyzed. Artificial Neural Network, k-Nearest Neighbors and Random Forest based models have been compared after training with more than 1-year hourly Energy Consumption data and other influence variables. The Random Forest achieved the best accuracy when re-trained, showing a MAPE below 10%. The importance of passing a detailed working calendar to the model, using accurate weather variables forecasts and defining an adequate re-training strategy have been proved to improve model accuracy

Drift-Aware Methodology for Anomaly Detection in Smart Grid

Energy efficiency and sustainability are important factors to address in the context of smart cities. In this sense, smart metering and nonintrusive load monitoring play a crucial role in fighting energy thefts and for optimizing the energy consumption of the home, building, city, and so forth. The estimated number of smart meters will exceed 800 million by 2020. By providing near real-time data about power consumption, smart meters can be used to analyze electricity usage trends and to point out anomalies guaranteeing companies' safety and avoiding energy wastes. In literature, there are many proposals approaching the problem of anomaly detection. Most of them are limited because they lack context and time awareness and the false positive rate is affected by the change in consumer habits. This research work focuses on the need to define anomaly detection method capable of facing the concept drift, for instance, family structure changes; a house becomes a second residence, and so forth. The proposed methodology adopts long short term memory network in order to profile and forecast the consumers' behavior based on their recent past consumptions. The continuous monitoring of the consumption prediction errors allows us to distinguish between possible anomalies and changes (drifts) in normal behavior that correspond to different error motifs. The experimental results demonstrate the suitability of the proposed framework by pointing out an anomaly in a near real-time after a training period of one week

Short-term Building Energy Model Recommendation System: A Meta-learning Approach

High-fidelity and computationally efficient energy forecasting models for building systems are needed to ensure optimal automatic operation, reduce energy consumption, and improve the building’s resilience capability to power disturbances. Various models have been developed to forecast building energy consumption. However, given buildings have different characteristics and operating conditions, model performance varies. Existing research has mainly taken a trial-and-error approach by developing multiple models and identifying the best performer for a specific building, or presumed one universal model form which is applied on different building cases. To the best of our knowledge, there does not exist a generalized system framework which can recommend appropriate models to forecast the building energy profiles based on building characteristics. To bridge this research gap, we propose a meta-learning based framework, termed Building Energy Model Recommendation System (BEMR). Based on the building’s physical features as well as statistical and time series meta-features extracted from the operational data and energy consumption data, BEMR is able to identify the most appropriate load forecasting model for each unique building. Three sets of experiments on 48 test buildings and one real building were conducted. The first experiment was to test the accuracy of BEMR when the training data and testing data cover the same condition. BEMR correctly identified the best model on 90% of the buildings. The second experiment was to test the robustness of the BEMR when the testing data is only partially covered by the training data. BEMR correctly identified the best model on 83% of the buildings. The third experiment uses a real building case to validate the proposed framework and the result shows promising applicability and extensibility. The experimental results show that BEMR is capable of adapting to a wide variety of building types ranging from a restaurant to a large office, and gives excellent performance in terms of both modeling accuracy and computational efficiency