Energy Production Forecasting From Solar Photovoltaic Plants Based on Meteorological Parameters for Qassim Region, Saudi Arabia

Muhannad Alaraj,Ibrahim Alsaidan,Mohammad Rizwan,Astitva Kumar,Majid Jamil

doi:10.1109/access.2021.3087345

Muhannad Alaraj, Ibrahim Alsaidan + Show 3 more

Open Access

https://doi.org/10.1109/access.2021.3087345

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Abstract

Due to the increasing cost of crude oil because of pandemic COVID-19 and global environmental threats, the exploitation of fossil fuels for power generation is discouraged. Further, the demand for electrical power is increasing drastically, and therefore, the exploitation of renewable energy resources, particularly solar photovoltaic-based technology for power generation is invigorated. However, the large-scale penetration of solar photovoltaic is becoming a major challenge in terms of stability, reliability of power when integrated with the grid. Thus, it becomes important to develop a novel approach or strategy which is useful to improve power quality, reliability, and grid stability. Solar photovoltaic power forecasting is a key tool for this new era and becoming the main component for a smart grid environment. Here, in this paper, the ensemble trees approach-based machine learning approach is utilized to forecast the solar photovoltaic power with the help of various meteorological parameters. The high-quality measured data for meteorological parameters for Qassim, Saudi Arabia is used in this research. The performance of the proposed model is evaluated with the help of statistical indices such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), Training Time (TT) and found within the desired limits. To validate the obtained results a comparative analysis with other machine learning models is carried out. Moreover, the proposed research may provide the roadmap in achieving the vision 2030 of the government of Saudi Arabia.

Highlights

Fossil fuel consumption in the last twenty years has increased exponentially leading to a huge environmental and energy crisis
The use of five different machine learning (ML) approaches is being implement to create five different forecasting model used for predicting the solar photovoltaic (SPV) power
The indicators used for comparative analysis are Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), and Training Time (TT)

Summary

Introduction

Fossil fuel consumption in the last twenty years has increased exponentially leading to a huge environmental and energy crisis. This rapid depletion of non-renewable energy sources has a drastic impact on the economic policies of governments, climatic conditions, and energy sustainability [1]. Almost two-thirds of CO2 emissions are from fossil fuel burning which leads to changing weather patterns and environmental deterioration. The uncontrolled burning of fossil fuel to meet the demand of the ever-growing population causes higher global temperatures and increased greenhouse gases (GHG) resulting in the greenhouse effect [2]. Electrical energy is a vital factor for facilitating economic growth, industrialization, urban development and technological advancement. The whole world has channeled its research resources on the application of renewable energy (RE) resources

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