Solar Irradiance Forecasting Using an Artificial Intelligence Model

P. K. Ray, Anindya Bharatee, P. S. Puhan, Sourav Sahoo
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Abstract

The paper presents the solar energy prediction using ANN in order to effectively predict solar irradiance. With increasing interest in the scope for renewable energy, many countries are adopting new technologies of solar photovoltaic which has higher solar resource potential. If in advance of 24 hours, solar irradiance can be predicted, then it would help us immensely to optimize the energy production efficiency. Traditional methods which were used involved empirical, analytical, and physics-based models, statistical forecasting of solar data, and numerical methods to effectively predict the amount of solar irradiation. With the increasing use of Machine Learning better predictive models are being developed which help us to forecast better thereby reducing the error and increasing the efficiency.
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利用人工智能模型预测太阳辐照度
为了有效地预测太阳辐照度,提出了利用人工神经网络进行太阳能预测的方法。随着人们对可再生能源领域的兴趣日益浓厚,许多国家正在采用具有更高太阳能资源潜力的太阳能光伏新技术。如果能提前24小时预测太阳辐照度,将极大地帮助我们优化能源生产效率。传统的方法包括经验、分析和基于物理的模型、太阳数据的统计预测和数值方法,以有效地预测太阳辐照量。随着机器学习的使用越来越多,更好的预测模型被开发出来,帮助我们更好地预测,从而减少误差,提高效率。
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