Power Demand Forecasting using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability

Eunjeong Choi, Soohwan Cho and Dong Keun Kim
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Eunjeong Choi: Department of Computer Science, Sangmyung University, Seoul 03016, Korea
Soohwan Cho: Department of Electrical Engineering, Sangmyung University, Seoul 03016, Korea
Dong Keun Kim: Department of Intelligent Engineering Information for Human, Institute of Intelligent Informatics Technology, Sangmyung University, Seoul 03016, Korea

Sustainability, 2020, vol. 12, issue 3, 1-14

Abstract: The purpose of this study is to design a novel custom power demand forecasting algorithm based on the LSTM Deep-Learning method regarding the recent power demand patterns. We performed tests to verify the error rates of the forecasting module, and to confirm the sudden change of power patterns in the actual power demand monitoring system. We collected the power usage data in every five-minute resolution in a day from some groups of the residential, public offices, hospitals, and industrial factories buildings in one year. In order to grasp the external factors and to predict the power demand of each facility, a comparative experiment was conducted in three ways; short-term, long-term, seasonal forecasting exp[eriments. The seasonal patterns of power demand usages were analyzed regarding the residential building. The overall error rates of power demand forecasting using the proposed LSTM module were reduced in terms of each facility. The predicted power demand data shows a certain pattern according to each facility. Especially, the forecasting difference of the residential seasonal forecasting pattern in summer and winter was very different from other seasons. It is possible to reduce unnecessary demand management costs by the designed accurate forecasting method.

Keywords: Short-term; seasonal forecasting; power demand forecasting; Deep-Learning; LSTM; smart grid; power usage patterns (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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