Occupant-Aware Energy Consumption Prediction in Smart Buildings Using a LSTM Model and Time Series Data

Muhammad Anan, Khalid Kanaan, Driss Benhaddou, Nidal Nasser, Basheer Qolomany, Hanaa Talei and Ahmad Sawalmeh ()
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Muhammad Anan: College of Engineering, Alfaisal University, Riyadh 11533, Saudi Arabia
Khalid Kanaan: Electrical and Computer Engineering Department, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
Driss Benhaddou: College of Engineering, Alfaisal University, Riyadh 11533, Saudi Arabia
Nidal Nasser: College of Engineering, Alfaisal University, Riyadh 11533, Saudi Arabia
Basheer Qolomany: Department of Internal Medicine, College of Medicine, Howard University, Washington, DC 20059, USA
Hanaa Talei: School of Sciences and Engineering, Al-Akhawayn University, Ifrane 53000, Morocco
Ahmad Sawalmeh: College of Engineering, Alfaisal University, Riyadh 11533, Saudi Arabia

Energies, 2024, vol. 17, issue 24, 1-15

Abstract: Accurate energy consumption prediction in commercial buildings is a challenging research task. Energy prediction plays a crucial role in energy efficiency, management, planning, sustainability, risk management, diagnosis, and demand response. Although many studies have been conducted on building energy predictions, the impact of occupancy on energy prediction models for office-type commercial buildings remains insufficiently explored, despite its potential to improve energy efficiency by 20%. This study investigates energy prediction using a Long Short-Term Memory (LSTM) model that incorporates time-series power consumption data and considers occupancy. A real-world dataset containing the per-minute electricity consumption of various appliances in an office building in Houston, TX, USA, is utilized. The proposed machine learning models forecast future energy consumption based on hourly, 3-hourly, daily, and quarterly predictions for individual appliances and total energy usage. The model’s performance is evaluated using the following three metrics: Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results demonstrate the superiority of the proposed system.

Keywords: ARIMA; LSTM; forecast; energy consumption; machine learning (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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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