Affiliation:
1. College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
2. Key Laboratory of Integrated Energy Optimization and Secure Operation of Liaoning Province, Northeastern University, Shenyang 110819, China
3. State Grid Electric Power Research Institute Wuhan Efficiency Evaluation Company Limited, Wuhan 430072, China
Abstract
Sustainability can achieve a balance among economic prosperity, social equity, and environmental protection to ensure the sustainable development and happiness of current and future generations; photovoltaic (PV) power, as a clean, renewable energy, is closely related to sustainability providing a reliable energy supply for sustainable development. To solve the problem with the difficulty of PV power forecasting due to its strong intermittency and volatility, which is influenced by complex and ever-changing natural environmental factors, this paper proposes a PV power forecasting method based on eXtreme gradient boosting (XGBoost)–sequential forward selection (SFS) and a double nested stacking (DNS) ensemble model to improve the stability and accuracy of forecasts. First, this paper analyzes a variety of relevant features affecting PV power forecasting and the correlation between these features and then constructs two features: global horizontal irradiance (GHI) and similar day power. Next, a total of 16 types of PV feature data, such as temperature, azimuth, ground pressure, and PV power data, are preprocessed and the optimal combination of features is selected by establishing an XGBoost–SFS to build a multidimensional climate feature dataset. Then, this paper proposes a DNS ensemble model to improve the stacking forecasting model. Based on the gradient boosting decision tree (GBDT), XGBoost, and support vector regression (SVR), a base stacking ensemble model is set, and a new stacking ensemble model is constructed again with the metamodel of the already constructed stacking ensemble model in order to make the model more robust and reliable. Finally, PV power station data from 2019 are used as an example for validation, and the results show that the forecasting method proposed in this paper can effectively integrate multiple environmental factors affecting PV power forecasting and better model the nonlinear relationships between PV power forecasting and relevant features. This is more applicable in the case of complex and variable environmental climates that have higher forecasting accuracy requirements.
Funder
National Natural Science Foundation of China
Applied Fundamental Research Program of Liaoning Province
Science and Technology Projects in Liaoning Province
Guangdong Basic and Applied Basic Research Foundation
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
Reference59 articles.
1. Rethinking of the “three elements of energy” toward carbon peak and carbon neutrality;Xin;Proc. CSEE,2022
2. An integrated review of factors influencing the perfomance of PV panels;Fouad;Renew. Sustain. Energy Rev.,2017
3. Review of PV power forecasting;Antonanzas;Sol. Energy,2016
4. Forecasting of PV power generation and model optimization: A review;Das;Renew. Sustain. Energy Rev.,2018
5. A comprehensive review and analysis of solar forecasting techniques;Singla;Front. Energy,2021
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