Pre-Feasibility Analysis of an Improved Sand Piper Optimization Convolution Neural Network (ISO-CNN) based Hybrid Solar and Biomass Energy System
Keywords:
achieved, compared, biomass, intermittency, predictionAbstract
The economic benefit and remarkable energy security can be achieved through the sustainable development of renewable energy and energy-efficient technology, which leads to the deduction in capital investment for the energy system. Renewable Energy System (RES) is a highly emerging attention-seeking area in the current economic condition because of an inexhaustible source. The accuracy of solar prediction is lagged due to the volatility and intermittency in solar forecasts. The installed power of biomass is below its potential even though highly available biomass source. So pre, feasible analysis is required to predict both solar and biomass energy for the future based on the prior data collection. For that, the deep learning method of convolutional neural network (CNN) is introduced, which is optimized by an improved sandpiper optimization (ISO) for forecasting both photovoltaic (PV) and biomass energy. Based on the successive four-month data collection, the MATLAB platform is used to simulate the output power of the proposed system. The proposed output is compared with conventional optimization-based predictions and results in high predictive accuracy due to climatic conditions.
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