Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks

This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or nuclear-based sources. Literature also shows that shortage fossil fuels inevitable world will face this problem sooner later. Moreover, remote rural areas suffer from not being able reach traditional grid power electricity need alternative energy. A “hybrid-renewable-energy system” (HRES) involving different renewable resources can be used supply sustainable these areas. The uncertain nature intelligent ability network approach process complex inputs have inspired use ANN methods forecasting. Thus, study aims driven models approaches provide accurate predictions energy, like solar, wind, hydro-power generation. Various refinement architectures networks, “multi-layer perception” (MLP), “recurrent-neural network” (RNN), “convolutional-neural (CNN), well “long-short-term memory” (LSTM) models, been offered applications These are perform short-term time-series prior information influences its value future prediction.