Abstract
Distributed photovoltaic (PV) solar power plants are playing an increasing role as a power generation resource in the modern electricity grid. However, PVs pose significant challenges to grid planners, operators, owners, investors, aggregators, and other stakeholders. This is due to the high uncertainty of the PV output power, which is caused by its entire dependence on intermittent environmental factors. This has brought a serious problem to the power industry to integrate and manage power grids containing significant penetration of PVs. Thus, an enhanced PV power forecast is very important to operate these power grids efficiently and reliably. Most previous methodologies have focused on predicting the aggregate amount of potential solar power generation at the national or regional scale and ignored the distributed PVs that are installed primarily for local electric supply. Furthermore, a few research groups have carried out predictor selection before training predictive models. This paper proposes an adaptive hybrid predictor subset selection (PSS) strategy to obtain the most relevant and nonredundant predictors for enhanced short-term forecasting of the power output of distributed PVs. In the proposed strategy, the binary genetic algorithm (BGA) is applied for the feature selection process and support vector regression (SVR) is used for measuring the fitness score of the predictors. In order to validate the effectiveness of the proposed strategy, it is applied to actual distributed PVs located in the Otaniemi area of Espoo, Finland. The findings are compared with those achieved by other PSS techniques. The proposed strategy enhances the quality and efficiency of the predictor subset selection, with minimal chosen predictors to achieve enhanced prediction accuracy. It outperforms the other prediction selection methods. Besides, a configuration of an adaptive forecasting model is introduced and the performance tests are presented to further validate the impact of the PSS results for the PV power prediction accuracy enhancement.
Highlights
Installation of renewable energy resources, in particular solar energy, has received much attention globally due to several environmental protocols agreed by almost all countries as primary directives of the United Nation (UN)
Comparative validation, configuration of an adaptive PV power forecasting model based on the predictor subset selection (PSS) results and quantitative relevance analysis of the PSS results are presented
The devised binary genetic algorithm (BGA)-support vector regression (SVR) PSS has given a predictor subset that resulted in better fitness than the original predictor space with all the initial predictors
Summary
Installation of renewable energy resources, in particular solar energy, has received much attention globally due to several environmental protocols agreed by almost all countries as primary directives of the United Nation (UN). Solar power generation has significant environmental advantages and is a promising source of energy for the future, its uncertainty due to intermittency of weather variables makes it more challenging to utilize than the conventional generation sources. This is due to the uncertainty of the generation causes large problems on the power grid stability and control. Accurate prediction of PV power plays a key role in power grids containing a huge penetration of PV solar power
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