Extreme Learning Machines for Solar Photovoltaic Power Predictions

Sameer Al-Dahidi,Osama Ayadi,Bashar R Qawasmeh,Mohammad Alrbai,Jehad Adeeb

doi:10.3390/en11102725

Abstract

The unpredictability of intermittent renewable energy (RE) sources (solar and wind) constitutes reliability challenges for utilities whose goal is to match electricity supply to consumer demands across centralized grid networks. Thus, balancing the variable and increasing power inputs from plants with intermittent energy sources becomes a fundamental issue for transmission system operators. As a result, forecasting techniques have obtained paramount importance. This work aims at exploiting the simplicity, fast computational and good generalization capability of Extreme Learning Machines (ELMs) in providing accurate 24 h-ahead solar photovoltaic (PV) power production predictions. The ELM architecture is firstly optimized, e.g., in terms of number of hidden neurons, and number of historical solar radiations and ambient temperatures (embedding dimension) required for training the ELM model, then it is used online to predict the solar PV power productions. The investigated ELM model is applied to a real case study of 264 kWp solar PV system installed on the roof of the Faculty of Engineering at the Applied Science Private University (ASU), Amman, Jordan. Results showed the capability of the ELM model in providing predictions that are slightly more accurate with negligible computational efforts compared to a Back Propagation Artificial Neural Network (BP-ANN) model, which is currently adopted by the PV system owners for the prediction task.

Highlights

Jordan is an emerging middle eastern country
Once the optimum Extreme Learning Machines (ELMs) architecture has been defined, it has been applied on the test dataset, X test, and its performance is evaluated on the Ntest test patterns by considering the Root Mean Square Error (RMSE) (Equation (3)), the Mean Absolute Error (MAE) and the Weighted Mean Absolute Error (WMAE) performance metrics [42,43], using 10-fold CV:
Unlike the ELM operation, the internal parameters (i.e., w i, bi and β i of the i-th hidden neuron, In this work, we investigate the capability of a new learning algorithm for single-hidden layer i = 1, . . . , H) of the ANN are defined by resorting to the iterative error Back Propagation (BP) algorithm in which the ANN

Summary

Introduction

Jordan is an emerging middle eastern country. Jordan is non-oil producing with limited natural resources. The overall production of natural gas and crude oil covers less than 4% of the total national energy demand. The imported energy resources are a major burden on the country’s national economy. As a yearly average in the last four years, Jordan was spending more than 10% of its Gross Domestic Product (GDP) to cover the cost of achieving its energy demands with a large share focused on electricity generation [1,2,3,4,5]. Electricity consumption is growing due to increasing industrialization and fast-growing population; the latter is related to natural growth as well as due to refugees from Syria and Iraq

Objectives

Results

Conclusion