Method and device for predicting net load reserve capacity demand based on CatBoost and storage medium
A technology of reserve capacity demand and prediction method, which is applied in the field of net load reserve capacity demand evaluation, to achieve the effect of reducing over-fitting, improving prediction accuracy and improving stability
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Embodiment 1
[0065] like figure 1 As shown, this embodiment provides a CatBoost-based net load reserve capacity demand forecasting method, and the specific steps are as follows:
[0066] S1: Data set construction and division. Construct a forecast dataset of day-ahead net load spare capacity demand in a northwestern province from 2018 to 2021, including input features x i and the corresponding output feature y i ; the input feature x i =[x i1 ,x i2 ,x i3 ,x i4 ,x i5 ] T It is the time x of the predicted daily history of a province obtained from the power grid control center i1 , month x i2 , solar term x i3 , whether it is a holiday x i4 and load / new energy forecast value x i5 , ; the output feature y i It is the forecast error of daily load / new energy forecast. It should be noted that x i5 When representing the load forecast value, x i =[x i1 ,x i2 ,x i3 ,x i4 ,x i5 ] T Constitute the load error prediction feature information, the corresponding output feature y i r...
Embodiment 2
[0087] This embodiment provides a CatBoost-based net load reserve capacity demand forecasting method, which is different from Embodiment 1 in that: when constructing a training set and a test set, the time series samples before April 12, 2021 are used as training set, using April 12-30, 2021 as the test set. Other principles and processes are the same as those in Embodiment 1, and are not repeated here.
[0088] Adopt the method of the present invention and the traditional method to compare, the evaluation result index comparison result is as shown in Table 2:
[0089] Table 2 Comparison of evaluation results and indicators
[0090]
[0091] From April 12 to 30, 2021, the forecast results of the day-ahead net load reserve capacity demand interval based on CatBoost are as follows Figure 4 As shown in Table 2, in terms of comprehensive indicators, the CatBoost ensemble learning method of the present invention has better performance than the traditional method. In this scen...
Embodiment 3
[0094] This embodiment provides a CatBoost-based net load reserve capacity demand forecasting method, which is different from Embodiment 1 in that: on August 2, 2021, the electricity load of the provincial power grid surged, and on August 1, 2021 The previous time series samples were used as the training set, and the summer heavy load period from August 1 to 7, 2021 was used as the test set, and rolling prediction was performed with a step size of 4h (16 moments). Other principles and processes are the same as those in Embodiment 1, and are not repeated here.
[0095] Adopt the method of the present invention and the traditional method to compare, the evaluation result index comparison result is as shown in Table 3:
[0096] Table 3 Comparison of evaluation results and indicators
[0097]
[0098] From August 1st to 7th, 2021, the forecast results of the daily net load reserve capacity demand interval based on CatBoost are as follows Figure 5 shown, from Figure 5 It ca...
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