Load prediction method considering demand response
A technology of load forecasting and demand response, which is applied in the field of power systems, can solve the problems of limited processing data scale and operating speed, and achieve the effect of improving the forecasting speed
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Embodiment 1
[0039] This embodiment provides a load forecasting method considering demand response, including the following steps:
[0040] Carry out cluster analysis on the historical sample data, filter out the historical time period with similar influence factors on the day to be predicted, and use it as the historical similar day;
[0041] Input the data of influencing factors of historical similar days and days to be predicted into the pre-trained load forecasting model, forecast demand response load and base load respectively, and add up the two parts of the forecast results to obtain the final load forecast result;
[0042] Wherein, the training method of the load forecasting model includes: establishing a load forecasting model based on the TCN algorithm, using the data of influencing factors of known load results as data samples, and training the established load forecasting model until convergence;
[0043] The known load results of the data samples include demand response load a...
Embodiment 2
[0062] This embodiment provides a load forecasting device considering demand response, which is characterized in that it includes:
[0063] The data analysis module is used to perform cluster analysis on the historical sample data, and filter out the historical period similar to the influencing factors on the day to be predicted, and use it as a historical similar day;
[0064] The load forecasting module is used to input the data of influencing factors of similar historical days and days to be forecasted into the pre-trained load forecasting model, forecast the demand response load and the basic load separately, and add the two parts of the forecast results to obtain the final load Prediction results; wherein, the load forecasting model is established based on the TCN algorithm, which is used to use the data of the influencing factors of known load results as data samples to train the established load forecasting model until convergence; the demand response load data is establ...
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