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Short- and Medium-Term Electricity Consumption Forecasting Using Prophet and GRU
Version 1
: Received: 18 September 2023 / Approved: 18 September 2023 / Online: 19 September 2023 (08:33:08 CEST)
A peer-reviewed article of this Preprint also exists.
Son, N.; Shin, Y. Short- and Medium-Term Electricity Consumption Forecasting Using Prophet and GRU. Sustainability 2023, 15, 15860. Son, N.; Shin, Y. Short- and Medium-Term Electricity Consumption Forecasting Using Prophet and GRU. Sustainability 2023, 15, 15860.
Abstract
This study proposes a short- and medium-term electricity consumption prediction algorithm by combining the GRU model suitable for long-term forecasting and the Prophet model suitable for seasonality and event handling. (1) Manufacturing Company B's Electricity consumption data and meteorological data in Naju, Jeollanam-do, South Korea are collected and preprocessed. (2) The preprocessed data proposes the Prophet model in the first step for seasonality and event handling prediction. (3) In the second step, seven multivariate data are experimented with GRU. Specifically, the seven multivariate data consist of six meteorological data and the residuals between the predicted data from the proposed Prophet model in Step 1 and the observed data. These are utilized to predict electricity consumption at 15-minute intervals. (4) Electricity consumption is predicted for short-term (2 days and 7 days) and medium-term (15 days and 30 days) scenarios. The experimental results demonstrate that the proposed method outperforms the conventional Prophet model by more than 23 times and the modified GRU model by more than 2 times in terms of MAPE.
Keywords
prophet; LSTM; GRU; meteorological data; electricity consumption forecasting
Subject
Computer Science and Mathematics, Computer Science
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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