Abstract
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.
Highlights
That the sample indexes for these sets are not identical in the four models, as the number and indexes of Convex Hull points (CH), which will be mandatorily integrated into each Tr, changes for each model
As observed from analysing the results presented in Section 5.3.1, the forecasting results obtained with the Multi-Objective Genetic Algorithm (MOGA) single solution are very good and are among the best results presented in the literature
MOGA ensembles with averaging solutions, 5.4
Summary
The impact of new approaches to design, develop, and manage energy systems while maintaining sustainability throughout their operation lifetime has been a significant challenge. Keywords such as “smart grids” and “nearly zero energy buildings” rose and are usually employed within the boundaries of the subsectors of energy systems, they should be analyzed in the context of the overall energy system [2]. Within the subject of modelling and simulating smart energy systems, energy forecasting plays an essential role in energy sector development, policy formulation, and the management of these systems [5]. A vast number of scientific reviews aim to elaborate the applications of forecasting models and techniques in different energy systems, as well as discussions on future trends.
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