Medium-Term Load Forecasting Using ANN and RNN in Microgrid Integrating Renewable Energy Source

The forecasting of load demand is one of the most important tools that can be used in this era of growing energy consumption by consumers in order to understand the future demands for power consumption by the consumers. By utilizing conventional techniques to their full potential, machine learning-based forecasting methods are now being developed to improve forecasting accuracy. Toward this end, this paper uses machine learning-based neural network methods. Here, artificial neural network (ANN) and recurrent neural networks (RNN) are used for forecasting strategy. The method is modeled in MATLAB and forecasting is done for the generation of a coal-fired generator in the microgrid which is considered. There are three input parameters considered in the modeling of ANN and RNN from microgrid: primary air fan load, plant load factor, and solar power generation.