Abstract
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.
Highlights
Photovoltaic (PV) power generation has achieved enormous development in recent years, mainly for becoming a significant component of the modern power industry’s decarbonization
Considering PV power generation in buildings, the PV panels are strongly evolving from off-grid systems to grid-connected systems associated with smart energy management systems (EMS) solutions
Another part of the fluctuations in solar irradiation availability is due to the presence of clouds, cloud mass, aerosol particle concentration, wind speed and direction, ambient temperature, among others, which stochastically reduce the PV panel power output [3,4]
Summary
Photovoltaic (PV) power generation has achieved enormous development in recent years, mainly for becoming a significant component of the modern power industry’s decarbonization. A part of the fluctuations is deterministic, due to location of the panels and rotational and translational movements of the planet in relation with the sun, being this part correctly described by physical equations [1], as may be found in [2]. Another part of the fluctuations in solar irradiation availability is due to the presence of clouds, cloud mass, aerosol particle concentration, wind speed and direction, ambient temperature, among others, which stochastically reduce the PV panel power output [3,4]. The PV panels’ output power depends on internal factors, as photovoltaic module temperature affects the radiation power conversion efficiency [3]
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