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  • Short-term forecasting photovoltaic solar power for home energy management systems
    Publication . Bot, Karol; Ruano, Antonio; Ruano, Maria
    Accurate photovoltaic (PV) power forecasting is crucial to achieving massive PV integration in several areas, which is needed to successfully reduce or eliminate carbon dioxide from energy sources. This paper deals with short-term multi-step PV power forecasts used in model-based predictive control for home energy management systems. By employing radial basis function (RBFs) artificial neural networks (ANN), designed using a multi-objective genetic algorithm (MOGA) with data selected by an approximate convex-hull algorithm, it is shown that excellent forecasting results can be obtained. Two case studies are used: a special house located in the USA, and the other a typical residential house situated in the south of Portugal. In the latter case, one-step-ahead values for unscaled root mean square error (RMSE), mean relative error (MRE), normalized mean average error (NMAE), mean absolute percentage error (MAPE) and R2 of 0.16, 1.27%, 1.22%, 8% and 0.94 were obtained, respectively. These results compare very favorably with existing alternatives found in the literature.
  • Design of ensemble forecasting models for home energy management systems
    Publication . Bot, Karol; Santos, Samira; Habou Laouali, Inoussa; Ruano, Antonio; Ruano, Maria da Graça
    The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models.
  • Forecasting electricity demand in households using MOGA-designed artificial neural networks
    Publication . Bot, Karol; Ruano, Antonio; Ruano, Maria
    The prediction of electricity demand plays an essential role in the building environment. It strongly contributes to making the building more energy-efficient, having the potential to increase both thermal and visual comfort of the occupants, while reducing energy consumption, by allowing the use of model predictive control. The present article focuses on the use of computational intelligence methods for prediction of the power consumption of a case study residential building, during a horizon of 12 hours. Two exogeneous variables (ambient temperature and day code) are used in the NARX model Two different time steps were considered in the simulations, as well as constrained and unconstrained model design. The study concluded that the smaller timestep and the constrained model design obtain the best power demand prediction performance. The results obtained compare very favourably with similar approaches in the literature Copyright (C) 2020 The Authors.
  • Recent techniques used in home energy management systems: a review
    Publication . Gomes, Isaías; Bot, Karol; Ruano, Maria; Ruano, Antonio
    Power systems are going through a transition period. Consumers want more active participation in electric system management, namely assuming the role of producers–consumers, prosumers in short. The prosumers’ energy production is heavily based on renewable energy sources, which, besides recognized environmental benefits, entails energy management challenges. For instance, energy consumption of appliances in a home can lead to misleading patterns. Another challenge is related to energy costs since inefficient systems or unbalanced energy control may represent economic loss to the prosumer. The so-called home energy management systems (HEMS) emerge as a solution. When well-designed HEMS allow prosumers to reach higher levels of energy management, this ensures optimal management of assets and appliances. This paper aims to present a comprehensive systematic review of the literature on optimization techniques recently used in the development of HEMS, also taking into account the key factors that can influence the development of HEMS at a technical and computational level. The systematic review covers the period 2018–2021. As a result of the review, the major developments in the field of HEMS in recent years are presented in an integrated manner. In addition, the techniques are divided into four broad categories: traditional techniques, model predictive control, heuristics and metaheuristics, and other techniques.
  • Home energy management systems with branch-and-bound model-based predictive control techniques
    Publication . Bot, Karol; Habou Laouali, Inoussa; Ruano, Antonio; Ruano, Maria
    At a global level, buildings constitute one of the most significant energy-consuming sectors. Current energy policies in the EU and the U.S. emphasize that buildings, particularly those in the residential sector, should employ renewable energy and storage and efficiently control the total energy system. In this work, we propose a Home Energy Management System (HEMS) by employing a Model-Based Predictive Control (MBPC) framework, implemented using a Branch-and-Bound (BAB) algorithm. We discuss the selection of different parameters, such as time-step, to employ prediction and control horizons and the effect of the weather in the system performance. We compare the economic performance of the proposed approach against a real PV-battery system existing in a household equipped with several IoT devices, concluding that savings larger than 30% can be obtained, whether on sunny or cloudy days. To the best of our knowledge, these are excellent values compared with existing solutions available in the literature.