研究目的
To predict the daily photovoltaic power production using air temperature, relative humidity, total horizontal solar radiation and diffuse horizontal solar radiation parameters as multi-tupled inputs.
研究成果
The grey wolf optimization algorithm-based multilayer perceptron model was found to be more successful and competitive for the daily photovoltaic power prediction. The study also revealed meaningful patterns about the constructed models, input tuples, and activation functions.
研究不足
The study focuses on daily photovoltaic power prediction and may not account for minute or hourly variations. The performance of the models may vary with different datasets or locations.
1：Experimental Design and Method Selection:
The study integrates grey wolf, ant lion, and whale optimization algorithms with multilayer perceptron models for photovoltaic power prediction.
2：Sample Selection and Data Sources:
Data from DKA Solar Center in Australia, including 365 one-day measurements of air temperature, relative humidity, total horizontal solar radiation, diffuse horizontal solar radiation, and photovoltaic power production.
3：List of Experimental Equipment and Materials:
Not explicitly mentioned.
4：Experimental Procedures and Operational Workflow:
The optimization algorithms provide biases and weights for the multilayer perceptron, which are then used to predict photovoltaic power.
5：Data Analysis Methods:
The prediction performance is analyzed using coefficient of determination (R2), mean absolute error (MAE), and mean absolute percentage error (MAPE).
独家科研数据包，助您复现前沿成果，加速创新突破
获取完整内容
