Annual Daily Average Load Curve Prediction Considering Dynamic Time Anchors and Typical Feature Constraints

doi:10.11930/j.issn.1004-9649.202308114

Abstract

Abstract:

Based on the trends, periodicity and calendar features of power load, it is realized to accurately predict the annual daily average load curves considering the dynamic time anchors and typical feature constraints, Firstly, a dynamic time anchor matrix is built based on the calendar association between the historical year and the target year. Then, based on the historical annual daily average load shape-factor curves obtained after normalization and periodic smoothing treatment, it is proposed to use the DTA-Soft-DBA to predict the target year's daily average load shape-factor curve. After inverse normalization and inverse periodic smoothing treatment, the annual daily average load prediction curves are obtained by correcting the typical feature constraints with the predicted value of power and electricity features. The case study results of an area in China show that the proposed method has higher prediction accuracy, and the results are consistent with the predicted values of typical features and the temporal variations within the target year. The proposed method can effectively integrate the common rules of historical sample time series with different calendar characteristics, which is reasonable and interpretable.

Key words: load forecasting, annual daily average load curve, Soft-DBA, feature constraints, calendar features

Dan LI, Shuai HE, Wei YAN, Yue HU, Zeren FANG, Yunyan LIANG. Annual Daily Average Load Curve Prediction Considering Dynamic Time Anchors and Typical Feature Constraints[J]. Electric Power, 2024, 57(11): 36-47.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202308114

https://www.electricpower.com.cn/EN/Y2024/V57/I11/36

Figures/Tables 12

Fig.1 The overall framework for annual daily average load curve forecasting based on FC-DTA-Soft-DBA

Fig.2 Weighted impact penalty matrix of 2015 to 2020 for the spring festival

Fig.3 Sub-influence areas of the time anchor matrix of 2015 to 2020

Fig.4 Load shape-factor curve prediction flow chart based on Soft-DBA with dynamic time anchor points

Fig.5 Daily and annual average load curves from 2015 to 2021

Fig.6 Average load curves from 2015 to 2019

Fig.7 The annual daily average load and holiday daily average load curves from 2015 to 2019

Fig.8 Comparison of daily average load shape-factor prediction curves by different methods

Fig.9 Comparison of daily average load prediction curves by different methods for 2021

Table 1 Evaluating indicator comparison of different methods for 2020 and 2021

方法	2020			2021			平均指标
方法	E_MAPE/%	E_RMSE/MW	R²	E_MAPE/%	E_RMSE/MW	R²	E_MAPE/%	E_RMSE/MW	R²
Holt-Winters	14.47	1856.94	0.28	8.51	1555.69	0.44	11.49	1706.32	0.36
GM	12.45	1803.01	0.32	11.28	2012.11	0.06	11.86	1907.56	0.19
Naive	12.00	1773.68	0.34	11.74	2172.00	–0.09	11.87	1972.84	0.13
ED	11.98	1685.25	0.4	8.32	1662.38	0.36	10.15	1673.82	0.38
DBA	12.32	1766.68	0.35	8.56	1714.12	0.32	10.44	1740.40	0.34
SGD-DBA	12.17	1728.36	0.37	8.51	1694.65	0.34	10.34	1711.51	0.36
Soft-DDBA	11.10	1477.22	0.54	7.90	1480.13	0.49	9.50	1478.68	0.52
FC-DTA-Soft-DBA	6.87	1054.16	0.77	6.79	1591.24	0.41	6.83	1322.70	0.59

Fig.10 Mean error comparison of FC-DTA-Soft-DBA ablation experiments in 2020 and 2021

Fig.11 Comparison of annual max/min and monthly average prediction load of ablation experiments in 2021

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