Prediction of energy consumption in campus buildings using long short-term memory


Muhamad Fisol, Muhammad Faiq and Tan, Kim Geok and Liew, Chia Pao and Hossain, Ferdous and Tso, Chih Ping and Lim, Li Li and Wong, Adam Yoon Khang and Mohd Shah, Zulhilmi (2023) Prediction of energy consumption in campus buildings using long short-term memory. Alexandria Engineering Journal, 67. pp. 65-76. ISSN 1110-0168

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In this paper, Long Short-Term Memory (LSTM) was proposed to predict the energy consumption of an institutional building. A novel energy usage prediction method was demonstrated for daily day-ahead energy consumption by using forecasted weather data. It used weather forecasting data from a local meteorological organization, the Malaysian Meteorological Department (MET). The predictive model was trained by considering the dependencies between energy usage and weather data. The performance of the model was compared with Support Vector Regression (SVR) and Gaussian Process Regression (GPR). The experimental results with a dataset obtained from a building in Multimedia University, Malacca Campus from January 2018 to July 2021 outperformed the SVR and GPR. The proposed model achieved the best RMSE scores (561.692–592.319) when compared to SVR (3135.590–3472.765) and GPR (1243.307–1334.919). Through experimentation and research, the dropout method reduced overfitting significantly. Furthermore, feature analysis was done with SHapley Additive exPlanation to identify the most important weather variables. The results showed that temperature, wind speed, rainfall duration and the amount had a positive effect on the model. Thus, the proposed approach could aid in the implementation of energy policies because accurate predictions of energy consumption could serve as system fault detection and diagnosis for buildings.

Item Type: Article
Uncontrolled Keywords: Energy consumption, Long short-term memory, Support vector regression, Gaussian process regression, Weather forecasting
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD9000-9999 Special industries and trades
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 31 Jan 2023 06:35
Last Modified: 31 Jan 2023 06:35


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