研究目的
To propose an accurate short-term photovoltaic power prediction method based on extreme learning machine and intelligent optimizer to improve the economic dispatch of power systems and the development of clean energy.
研究成果
The ICSO-ELM model has a better forecasting effect with average RMSE and MAPE values of 5.54% and 3.08% under three different weather conditions. The proposed method is of great significance for the economic dispatch of power systems and the development of clean energy.
研究不足
The study does not take into account many extreme weather conditions (such as haze, ice and snow). Future studies should focus on PV power prediction under extreme weather conditions and improve the stability of the prediction model.
1：Experimental Design and Method Selection:
The study proposes a new short-term photovoltaic power output prediction model based on extreme learning machine and intelligent optimizer. The input of the model is determined by correlation coefficient method. The chicken swarm optimizer is improved to strengthen the convergence.
2：Sample Selection and Data Sources:
The experimental data is from the Desert Knowledge Australia Solar Centre (DKASC), including sunny, cloudy, and rainy weather conditions.
3：List of Experimental Equipment and Materials:
Not explicitly mentioned.
4：Experimental Procedures and Operational Workflow:
The improved chicken swarm optimizer is used to optimize the weights and the extreme learning machine thresholds. The model is then used to predict the photovoltaic power under different weather conditions.
5：Data Analysis Methods:
The mean absolute percentage error (MAPE) and root mean square error (RMSE) are used to evaluate the prediction effect of prediction models. Decision coefficient (R2) is used to judge the fitting degree.
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
