研究目的
Investigating the combined forecasting method based on ABC-SVM and PSO-RF for the prediction of photovoltaic power generation in microgrid.
研究成果
The combined forecasting method based on ABC-SVM and PSO-RF significantly improves the prediction accuracy of photovoltaic power generation in microgrids, especially under different meteorological conditions. It provides a practical reference for optimal dispatch of micro-grid and photovoltaic output.
研究不足
The study does not mention the computational cost or the scalability of the proposed method to larger datasets or different geographical locations.
1：Experimental Design and Method Selection:
The study uses ABC-SVM for meteorological classification and PSO-RF for photovoltaic power generation forecasting under different meteorological conditions.
2：Sample Selection and Data Sources:
Historical meteorological and photovoltaic output data from a microgrid station in Yangjiang, Guangdong Province, are used.
3：List of Experimental Equipment and Materials:
Not explicitly mentioned.
4：Experimental Procedures and Operational Workflow:
Data cleaning and feature extraction followed by training with ABC-SVM and PSO-RF models.
5：Data Analysis Methods:
The accuracy of the models is compared using MAPE and RMSE metrics.
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