Abstract
Forecasting the power production from renewable energy sources (RESs) has become fundamental in microgrid applications to optimize scheduling and dispatching of the available assets. In this article, a methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented. The goal of this paper is to provide a robust methodology to forecast 24 h in advance the PV power production in a microgrid, addressing the specific criticalities of this environment. The proposed approach has to validate measured data properly, through an effective algorithm and further refine the power forecast when newer data are available. The procedure is fully implemented in a facility of the Multi-Good Microgrid Laboratory (MG L a b 2 ) of the Politecnico di Milano, Milan, Italy, where new Energy Management Systems (EMSs) are studied. Reported results validate the proposed approach as a robust and accurate procedure for microgrid applications.
Highlights
The power uncertainty exhibited by many Renewable Energy Sources (RES) as PV and wind represents a huge challenge for the stability, security, and reliability of integrated electricity systems [1]
The proposed methodology was divided into sub-tasks that were singularly evaluated and discussed throughout the work
The importance of filtering the available measurements to compose the historical dataset needed for the physical hybrid artificial neural network training was shown
Summary
PV power fluctuations depend on two factors: the first one is deterministic due to Earth’s revolution around the Sun, while the second is stochastic and depends on atmospheric conditions as cloud cover, dust, pollution, or local shadows on PV modules [2]. In this frame, forecasting power production from RES can greatly help the management and the operation of modern energy systems as, for example, microgrids. Statistical methods, on the other hand, require historical data of solar irradiance and power production to infer trends They are further divided into two categories: Artificial Intelligence (AI)
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