Short-Term Consumer Load Forecasting Based on Mutual Information and LSTM

doi:10.12204/j.issn.1000-7229.2022.07.011

Abstract

Abstract:

Compared with the system-level load, consumer load has the characteristics of small base, stronger volatility and randomness, which increases the difficulty of consumer load forecasting. With the help of mutual information and deep learning theory, this paper proposes a short-term consumer load forecasting model based on max-relevance and min-redundancy (mRMR) and long short-term memory network (LSTM). Firstly, the mRMR algorithm is used to sort the characteristic variables and select a suitable set of input variables. mRMR can not only ensure the maximum mutual information value between the input variable and the target value, but also minimize the redundancy between the variables. Secondly, the LSTM forecasting model is established for the selected set of input variables. LSTM can better process and forecast time series with long delays, and there will be no gradient disappearance and gradient explosion. Finally, an example is used to verify the effectiveness of the algorithm in this paper.

Key words: short-term consumer load forecasting, mutual information, max-relevance and min-redundancy algorithm, long short-term memory network

CLC Number:

TM715

ZHONG Jingsong, WANG Shaolin, RAN Yi, RAN Xintao, YU Jinping, YU Haimeng. Short-Term Consumer Load Forecasting Based on Mutual Information and LSTM[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(7): 96-102.

Figures/Tables 9

Fig. 1 LSTM structure model

Fig.2 Forecasting error curves of different models of consumer load

Fig.3 True value of user load and model prediction value (August 9th, 2016)

Table 1 Error statistics of consumer load forecasting 24 hours in advance (August 9th,2016)

Fig.4 Real value of user load and the predicted value 24 h in advance(Nov.20—26, 2016)

Fig.5 The real value of user load and the predicted value 1h in advance(Nov.20—26, 2016)

Table 2 Error statistics of consumer load forecasting 24 hours and 1 hour in advance (Nov.20—26, 2016)

Fig.6 Error distribution of consumer load forecasting 24 hours in advance

Fig.7 The error distribution of consumer load forecasting 1 hour in advance

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