Abstract
The current research paper deals with the worldwide problem of photovoltaic (PV) power forecasting by this innovative contribution in short-term PV power forecasting time horizon based on classification methods and nonlinear autoregressive with exogenous input (NARX) neural network model. In the meantime, the weather data and PV installation parameters are collected through the data acquisition systems installed beside the three PV systems. At the same time, the PV systems are located in Morocco country, respectively, the 2 kWp PV installation placed at the Higher Normal School of Technical Education (ENSET) in Rabat city, the 3 kWp PV system set at Nouasseur Casablanca city, and the 60 kWp PV installation also based in Rabat city. The multisite modelling approach, meanwhile, is deployed for establishing the flawless short-term PV power forecasting models. As a result, the implementation of different models highlights their achievements in short-term PV power forecasting modelling. Consequently, the comparative study between the benchmarking model and the forecasting methods showed that the forecasting techniques used in this study outperform the smart persistence model not only in terms of normalized root mean square error (nRMSE) and normalized mean absolute error (nMAE) but also in terms of the skill score technique applied to assess the short-term PV power forecasting models.
Highlights
Introduction e reports by Renewables2017 Global Status and International Energy Agency (IEA) confirmed that the solar PV power has grown tremendously which implied many economic and social benefits. e cumulative solar PV capacity, reached 398 GW which generated over 460 TWh and represented around 2% of global power energy [1]
The forecasting methods can help the integration of natural and sustainable energy resources and encourage the adoption of recent energy systems such as microgrids, which are smart small microgenerations based on microsources including the renewable energy. e microgrids, request advanced techniques of control and forecasting to overcome the effect of solar PV variability
Simulation Results. is research paper provides the best results based on simulation of two kinds of model that belong to two different areas of artificial intelligence modelling. e first part of simulation concerns the results of artificial neural network application, the use of nonlinear autoregressive with exogenous input (NARX) neural network model, and the second part of simulation concerns the application of classification methods, the use of K-nearest neighbors with similarity algorithm to forecast the short-term PV power
Summary
Introduction e reports by Renewables2017 Global Status and International Energy Agency (IEA) confirmed that the solar PV power has grown tremendously which implied many economic and social benefits. e cumulative solar PV capacity, reached 398 GW which generated over 460 TWh and represented around 2% of global power energy [1]. The strong penetration of solar PV energy in the global energy mix has driven the thinking to generation of electrical power grids and the renovation of most existence electrical grids to host the new mode of solar PV and Journal of Electrical and Computer Engineering guaranteeing its integration. In this case, the need to smart energy management systems (SEMS) that incorporate the forecasting methods of solar PV power is an important key to overcome many trials of renewable energy challenges and allow them (especially the PV power) the flexibility in terms of control and monitoring.
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