Occupant-Aware Energy Consumption Prediction in Smart Buildings Using a LSTM Model and Time Series Data
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 ene...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/24/6451 |
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| _version_ | 1850049607584710656 |
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| author | Muhammad Anan Khalid Kanaan Driss Benhaddou Nidal Nasser Basheer Qolomany Hanaa Talei Ahmad Sawalmeh |
| author_facet | Muhammad Anan Khalid Kanaan Driss Benhaddou Nidal Nasser Basheer Qolomany Hanaa Talei Ahmad Sawalmeh |
| author_sort | Muhammad Anan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-34a9aed6e0ae44ada7bce9c9509d49d2 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-34a9aed6e0ae44ada7bce9c9509d49d22025-08-20T02:53:41ZengMDPI AGEnergies1996-10732024-12-011724645110.3390/en17246451Occupant-Aware Energy Consumption Prediction in Smart Buildings Using a LSTM Model and Time Series DataMuhammad Anan0Khalid Kanaan1Driss Benhaddou2Nidal Nasser3Basheer Qolomany4Hanaa Talei5Ahmad Sawalmeh6College of Engineering, Alfaisal University, Riyadh 11533, Saudi ArabiaElectrical and Computer Engineering Department, King Abdullah University of Science and Technology, Thuwal 23955, Saudi ArabiaCollege of Engineering, Alfaisal University, Riyadh 11533, Saudi ArabiaCollege of Engineering, Alfaisal University, Riyadh 11533, Saudi ArabiaDepartment of Internal Medicine, College of Medicine, Howard University, Washington, DC 20059, USASchool of Sciences and Engineering, Al-Akhawayn University, Ifrane 53000, MoroccoCollege of Engineering, Alfaisal University, Riyadh 11533, Saudi ArabiaAccurate 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.https://www.mdpi.com/1996-1073/17/24/6451ARIMALSTMforecastenergy consumptionmachine learning |
| spellingShingle | Muhammad Anan Khalid Kanaan Driss Benhaddou Nidal Nasser Basheer Qolomany Hanaa Talei Ahmad Sawalmeh Occupant-Aware Energy Consumption Prediction in Smart Buildings Using a LSTM Model and Time Series Data Energies ARIMA LSTM forecast energy consumption machine learning |
| title | Occupant-Aware Energy Consumption Prediction in Smart Buildings Using a LSTM Model and Time Series Data |
| title_full | Occupant-Aware Energy Consumption Prediction in Smart Buildings Using a LSTM Model and Time Series Data |
| title_fullStr | Occupant-Aware Energy Consumption Prediction in Smart Buildings Using a LSTM Model and Time Series Data |
| title_full_unstemmed | Occupant-Aware Energy Consumption Prediction in Smart Buildings Using a LSTM Model and Time Series Data |
| title_short | Occupant-Aware Energy Consumption Prediction in Smart Buildings Using a LSTM Model and Time Series Data |
| title_sort | occupant aware energy consumption prediction in smart buildings using a lstm model and time series data |
| topic | ARIMA LSTM forecast energy consumption machine learning |
| url | https://www.mdpi.com/1996-1073/17/24/6451 |
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