A pruning approach to optimize synaptic connections and select relevant input parameters for neural network modelling of solar radiation

Abstract

The accurate modelling of solar radiation is important for many applications including agriculture and energy management. Previous research has widely focused on the development of artificial neural network (ANN) models for this task. Since the inputs to these models include different meteorological parameters, it is usually time consuming and costly to acquire these parameters. Therefore, selection of most relevant input parameters is a key step in the construction of these models. The overall goal of this research is to develop ANN models with less number of parameters and at the same time, high modelling accuracy. To this aim, an algorithm for network pruning, the optimal brain surgeon (OBS), is proposed to achieve two main objectives, selection of most relevant input parameters and optimization of the network structure at synaptic level. Four meteorological parameters, namely: temperature (T), relative humidity (RH), wind speed (WS), and sunshine duration (SSD) are used to model solar radiation, the global horizontal irradiation (GHI), over Abu Dhabi, the United Arab Emirates. The results show that the least relevant input parameter is RH with a contribution of 15.2% in the modelling process as compared to 24.8% for WS, 47.8% for T, and 54.7% for SSD. The parameter selection results coincide with recently used techniques in solar radiation research, J48 technique in Waikato Environment for Knowledge Analysis (WEKA) software and Automatic Relevance Determination (ARD) method. The modelling performance is compared with an all-connected ANN using all the four input parameters and ten hidden layer neurons with a total of 61 synaptic connections including biases. On the one hand, the proposed technique has successfully achieved an ANN with three inputs (T, WS, SSD), seven active hidden layer neuron, and 17 synaptic connections including biases. On the other hand, there are improvements observed in statistical evaluation metrics including mean absolute biased error (MABE), adjusted coefficient of determination (R¯2), Akaike information criterion (AIC), Akaike final prediction error (FPE), Rissanen's minimum description length (MDL), and cross entropy (CE).