Dual Deep Learning Networks Based Load Forecasting with Partial Real-Time Information and Its Application to System Marginal Price Prediction

Khikmafaris Yudantaka, Jung-Su Kim and Hwachang Song
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Khikmafaris Yudantaka: Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea
Jung-Su Kim: Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea
Hwachang Song: Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea

Energies, 2019, vol. 13, issue 1, 1-17

Abstract: Load power forecast is one of most important tasks in power systems operation and maintenance. Enhancing its accuracy can be helpful to power systems scheduling. This paper presents how to use partial real-time temperature information in forecasting load power, which is usually done using past load power and temperature data. The partial real-time temperature information means temperature information for only part of the entire prediction time interval. To this end, a long short-term memory (LSTM) network is trained using past temperature and load power data in order to forecast load power, where forecasted load power depends on the temperature prediction implicitly. Then, in order to deal with the case where nontrivial temperature prediction errors happen, a multi-layer perceptron (MLP) network is trained using the past data describing the relation between temperature variation and load power variation. Then, the temperature is measured at the beginning of the prediction time-interval and compensated load forecast is computed by adding the output of the LSTM and that of the MLP whose input is the temperature prediction error. It is shown that the proposed compensation using the real-time temperature information indeed improves performance of load power forecast. This improved load forecast is used to predict system marginal price (SMP). The proposed method is validated using the real temperature and load power data of South Korea.

Keywords: load forecast; SMP (system marginal price) forecast; LSTM (long short-term memory); MLP (multi-layer perceptron) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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