Feasibility study of a novel methodology for solar radiation prediction on an hourly time scale: A case study in Plymouth, United Kingdom

Abstract

Accurate forecasting of renewable energies provides a suitable means for efficient and secure operation of electricity grids and enables optimal energy market planning. This paper presents a number of novel prediction models for solar radiation prediction; and their performances are compared to those of conventional prediction models such as Neural Networks (NNs). All the developed models are based on linear predictive coding principles and image processing fundamentals using one and two-dimensional (1-D & 2-D) Finite Impulse Response (FIR) filters. The studies of highly correlated solar radiations among different hours of a day and also among the identical hours of different days are important parts of the developed prediction models. The meteorological data was collected for the duration of May 1, 2011 to April 30, 2012 from the Casella automatic weather station at Plymouth, UK. To prove the efficiency of the proposed prediction models, some similar data are used for all of the comparing predictive algorithms. It was observed that the performance of the 2-D FIR filters were better than that of the 1-D representations and NNs. However, their prediction quality greatly depends on the filter size and their related optimal tap weights.