Medium- and Long-Term Load Forecasting for Power Plants Based on Causal Inference and Informer
نویسندگان
چکیده
Accurate forecasting of power plant loads is critical for maintaining a stable supply, minimizing grid fluctuations, and enhancing market trading mechanisms. However, the data on generation load (hereinafter abbreviated as load) are non-stationary. The focus existing methods has been continuously improving ability to capture dependent coupling between outputs inputs, while research external factors, which causes non-stationary data, neglected. identification causal relationship variables significant factor in accurately predicting load. In present study, effects various were identified then quantitatively calculated using methods. Based improved Informer model, long-time series hybrid method was proposed called inference-improved Causal–Informer). mutual information used remove insignificant variables. Subsequently, factors such GDP, holidays, ambient temperature, wind speed, maintenance status, rainfall selected input features model. Finally, Causal–Informer evaluated historical East China. Compared with four popular models, measurements Root Mean Squared Error (RMSE), Absolute (MAE) Percentage (MAPE) reduced by 89.8 million kwh–672.3 kwh, 56.8 kwh–637.9 5.1–25.4%. achieved most accurate results. MAPE reached 10.4% 24.8% 30 time steps ahead 90 forecasts, respectively.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2023
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app13137696