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.

The electrification prospect in some rural areas in Malaysia is limited because of no access to grid connection. This challenge has aroused concerns among researchers and energy providers in finding an alternative source of energy. A hybrid renewable energy system (HRES) deems as a good alternative to overcome the problem. This study employs a linear programming model in estimating socioeconomic and techno-economic analysis of HRES at Tanjung Labian. The target location is a residential area in Sabah, with the major source of income comes from timber as most of the residents are engaged in forestry activities. The socioeconomic evaluation of this study reveals the minimum values of expected demand not served (EDNS) and loss of load probability (LOLP) from the hybrid PV-diesel with battery configuration and the result contributes to the highest number of students who passed the examination. Additionally, the results of the study also reveal that hybrid PVdiesel with battery configuration is the most economical and most environmentally friendly system when it is compared to other configurations. The results of this work may encourage the adoption of a hybrid renewable energy system with a battery system by replacing and upgrading existing standalone diesel generators system in Malaysia.