Solar irradiance measurement instrumentation and power solar generation forecasting based on Artificial Neural Networks (ANN): A review of five years research trend.,Science of the Total Environment

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Solar irradiance measurement instrumentation and power solar generation forecasting based on Artificial Neural Networks (ANN): A review of five years research trend.
Science of the Total Environment ( IF 8.2 ) Pub Date : 2020-01-22 , DOI: 10.1016/j.scitotenv.2020.136848
Abdul Rahim Pazikadin ₁ , Damhuji Rifai ₁ , Kharudin Ali ₂ , Muhammad Zeesan Malik ₃ , Ahmed N Abdalla ₄ , Moneer A Faraj ₅

Affiliation

The increased demand for solar renewable energy sources has created recent interest in the economic and technical issues related to the integration of Photovoltaic (PV) into the grid. Solar photovoltaic power generation forecasting is a crucial aspect of ensuring optimum grid control and power solar plant design. Accurate forecasting provides significant information to grid operators and power system designers in generating an optimal solar photovoltaic plant and to manage the power of demand and supply. This paper presents an extensive review on the implementation of Artificial Neural Networks (ANN) on solar power generation forecasting. The instrument used to measure the solar irradiance is analysed and discussed, specifically on studies that were published from February 1st, 2014 to February 1st, 2019. The selected papers were obtained from five major databases, namely, Direct Science, IEEE Xplore, Google Scholar, MDPI, and Scopus. The results of the review demonstrate the increased application of ANN on solar power generation forecasting. The hybrid system of ANN produces accurate results compared to individual models. The review also revealed that improvement forecasting accuracy can be achieved through proper handling and calibration of the solar irradiance instrument. This finding indicates that improvements in solar forecasting accuracy can be increased by reducing instrument errors that measure the weather parameter.

中文翻译：

基于人工神经网络（ANN）的太阳辐照度测量仪器和发电量预测：五年研究趋势的回顾。

对太阳能可再生能源的需求增加，最近引起了人们对与将光伏（PV）并入电网相关的经济和技术问题的兴趣。太阳能光伏发电预测是确保最佳电网控制和太阳能电站设计的关键方面。准确的预测可以为电网运营商和电力系统设计人员提供重要信息，帮助他们生成最佳的太阳能光伏电站并管理供需电力。本文对人工神经网络（ANN）在太阳能发电量预测中的应用进行了广泛的综述。分析和讨论了用于测量太阳辐照度的仪器，特别是针对2014年2月1日至2019年2月1日发布的研究。选定的论文来自五个主要数据库，即直接科学，IEEE Xplore，Google Scholar，MDPI和Scopus。审查结果表明，人工神经网络在太阳能发电预测中的应用越来越广泛。与单个模型相比，人工神经网络的混合系统可产生准确的结果。审查还显示，可以通过正确处理和校准太阳辐照仪来实现改进的预测准确性。该发现表明，可以通过减少测量天气参数的仪器误差来提高太阳预报准确性。与单个模型相比，人工神经网络的混合系统可产生准确的结果。审查还显示，可以通过正确处理和校准太阳辐照仪来实现改进的预测准确性。该发现表明，可以通过减少测量天气参数的仪器误差来提高太阳预报准确性。与单个模型相比，人工神经网络的混合系统可产生准确的结果。审查还显示，可以通过正确处理和校准太阳辐照仪来实现改进的预测准确性。该发现表明，可以通过减少测量天气参数的仪器误差来提高太阳预报准确性。

更新日期：2020-01-22

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