Long-Term Price Forecasting for the Cost-Benefit Analysis of Power Equipment

Abstract

Forecasting of electricity price plays a significant role in electrical network planning and development. In the present paper, we offer three prediction approaches: the naive method (NM), Fourier transformation (FT) with imposition of white noise and the artificial neural network (ANN) model. Our research proves the possibility of using any of three approaches due to the high forecasting accuracy of all of them. A case study using three types of forecasting methods, real-life data and a model of the distribution grid of our native country is presented to demonstrate the efficiency of our investigation and used to estimate the income generated by the energy storage system.