Research Area: Machine Learning
Prediction of solar irradiance is an essential requirement for reliable planning and efficient designing of solar energy systems. Thus, in present work, a new ensemble model, which consists of two advance base models, namely extreme gradient boosting forest and deep neural networks (XGBF-DNN), is proposed for hourly global horizontal irradiance forecast. These base models are integrated using ridge regression to avoid the over-fitting problem. Moreover, the proposed framework is designed carefully to ensure the diversity in base models, as diversity among base models is widely recognized as a key to the success of an ensemble model. Further, to enhance the models accuracy, a subset of input features is selected, which includes temperature, clear-sky index, relative humidity, and hour of the day as the most relevant features. To provide a comprehensive and reliable assessment, proposed model is validated with data from three different climatic Indian locations. Subsequently, a seasonal analysis is also carried out to provide a deeper insight into the models performance. The performance of the proposed model is evaluated by comparing the prediction results with different models, including benchmark smart persistence and traditional machine learning techniques, such as random forest, support vector regression, extreme gradient boosting forest and deep neural networks. The proposed ensemble model exhibits the best combination of stability and prediction accuracy irrespective of seasonal variations in weather conditions and shows a forecast skill score in a range of about 33%–40% in prediction error. The predictive performance and stability make XGBF-DNN an ideal and reliable model for hourly global horizontal irradiance prediction. Therefore, the developed model can be advised to serve as a potential prediction model in several domains, such as forecasting of solar power, wind power, electricity consumption, etc.
Keywords:
Solar energy systems
Deep neural networks
Global horizontal irradiance
Machine learning techniques
Deep neural networks
Author(s) Name: Pratima Kumari, Durga Toshniwal
Journal name: Journal of Cleaner Production
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.jclepro.2020.123285
Volume Information: Volume 279
