Improved Deep Learning Model for Forecasting Short-Term Load Based on Random Forest Algorithm and Rough Set Theory

doi:10.12096/j.2096-4528.pgt.23013

Abstract

Abstract:

Accurate power load forecasting is conducive to ensuring the safe and economic operation of the power system. Aiming at the problems of low prediction accuracy and long time consuming of the current prediction algorithms, an improved deep learning (DL) short-term load forecasting model based on random forest (RF) algorithm and rough set theory (RST), namely RF-DL-RST, was proposed. Firstly, based on historical data, the model used RF algorithm to extract the key features that affected the load forecasting. Then, the key features and historical load data were trained as the input and output items of deep neural network (DNN), and the prediction results were corrected by RST. After that, the rough set method was used to revise the prediction results. Finally, the simulation was verified by an example. The results show that the prediction accuracy of the model is higher than that of a single DNN model and a model without RST revised.

Key words: power load forecasting, random forest (RF) algorithm, deep learning (DL), rough set theory (RST)

CLC Number:

TK 01

Yu FENG, Youbin SONG, Sheng JIN, Jiahuan FENG, Xuechen SHI, Yongjie YU, Xianchao HUANG. Improved Deep Learning Model for Forecasting Short-Term Load Based on Random Forest Algorithm and Rough Set Theory[J]. Power Generation Technology, 2023, 44(6): 889-895.

Figures/Tables 8

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影响因素	特征量	含义
时间因素	月	1—12月
	日	每月的具体日期
	工作日	正常上班，取值1
	节假日	周六日及其他节假日，取值0
	当日小时	00：00—24：00
天气因素	最高温度	当日最高温度，℃
	最低温度	当日最低温度，℃
	平均温度	当日平均温度，℃
	平均相对湿度	当日平均湿度，%
	天气条件	如晴、阴、雨、雪等
	空气质量	空气质量指数
	平均风速	当日平均风速，m/s
	日出时间	具体时刻
	日落时间	具体时刻
政策因素	是否封控	受疫情、天灾影响时取1，反之取0

影响因素	特征量	含义
时间因素	月	1—12月
	日	每月的具体日期
	工作日	正常上班，取值1
	节假日	周六日及其他节假日，取值0
	当日小时	00：00—24：00
天气因素	最高温度	当日最高温度，℃
	最低温度	当日最低温度，℃
	平均温度	当日平均温度，℃
	平均相对湿度	当日平均湿度，%
	天气条件	如晴、阴、雨、雪等
	空气质量	空气质量指数
	平均风速	当日平均风速，m/s
	日出时间	具体时刻
	日落时间	具体时刻
政策因素	是否封控	受疫情、天灾影响时取1，反之取0

模型	DL训练时间/s	MSE/MW²	MAE/%
RF-DL-RST	96.29	680.33	4.01
RF-DL	96.29	974.65	5.77
DL-RST	107.21	865.84	4.73

模型	DL训练时间/s	MSE/MW²	MAE/%
RF-DL-RST	96.29	680.33	4.01
RF-DL	96.29	974.65	5.77
DL-RST	107.21	865.84	4.73

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