Abstract
Solar power generation deals with uncertainty and intermittency issues that lead to some difficulties in controlling the whole grid system due to imbalanced power production and power demand. The forecasting of solar power is an effort in securing the integration of renewable energy into the grid. This work proposes a forecasting model called WT-ANFIS-HFPSO which combines the wavelet transform (WT), adaptive neuro-fuzzy inference system (ANFIS) and hybrid firefly and particle swarm optimization algorithm (HFPSO). In the proposed work, the WT model is used to eliminate the noise in the meteorological data and solar power data whereby the ANFIS is functioning as the forecasting model of the hourly solar power data. The HFPSO is the hybridization of the firefly (FF) and particle swarm optimization (PSO) algorithm, which is employed in optimizing the premise parameters of the ANFIS to increase the accuracy of the model. The results obtained from WT-ANFIS-HFPSO are then compared with several other forecasting strategies. From the comparative analysis, the WT-ANFIS-HFPSO showed superior performance in terms of statistical error analysis, confirming its reliability as an excellent forecaster of hourly solar power data.
Highlights
Global warming is a modern crisis due to the vast consumption of fossil fuels for the generation of electricity
The proposed model is different from the traditional forecasting model as it is a perfect combination of the following parts: (1) a wavelet transform (WT) to remove the noises in the panel temperature data, tilted radiation data, global radiation data and solar power data; (2) an adaptive neuro-fuzzy inference system (ANFIS) as the forecasting tool of future solar power data; and (3) hybrid firefly and particle swarm optimization algorithm (HFPSO) to optimize the premise parameters of the ANFIS model
A model which is known as the WT-ANFIS-HFPSO, was developed which aims to achieve supreme model performance for solar power forecasting
Summary
Global warming is a modern crisis due to the vast consumption of fossil fuels for the generation of electricity. Sumithira et al employed the ANFIS model to predict monthly solar radiation for 31 districts in Tamil Nadu, India and the proposed model demonstrated a better result when compared with other methods used by previous works [16]. All of the above-mentioned works of literature which combine a forecasting model with an optimization algorithm have given better accuracy during forecasting Those hybrid methods do not consider the data filtration technique used to eliminate noise in the data. According to the following assumptions, this paper takes into account a solar power forecasting model that utilizes the WT as a denoising technique, the ANFIS model as a forecasting tool and the hybrid firefly particle swarm optimization (HFPSO) as a hybrid optimization algorithm.
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