Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building
Electric energy consumption forecasting is an interesting, challenging, and important issue in energy management and equipment efficiency improvement. Existing approaches are predictive models that have the ability to predict for a specific profile, i.e., a time series of a whole building or an indi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/9/2668 |
| _version_ | 1797568621379059712 |
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| author | Tuong Le Minh Thanh Vo Tung Kieu Eenjun Hwang Seungmin Rho Sung Wook Baik |
| author_facet | Tuong Le Minh Thanh Vo Tung Kieu Eenjun Hwang Seungmin Rho Sung Wook Baik |
| author_sort | Tuong Le |
| collection | DOAJ |
| description | Electric energy consumption forecasting is an interesting, challenging, and important issue in energy management and equipment efficiency improvement. Existing approaches are predictive models that have the ability to predict for a specific profile, i.e., a time series of a whole building or an individual household in a smart building. In practice, there are many profiles in each smart building, which leads to time-consuming and expensive system resources. Therefore, this study develops a robust framework for the Multiple Electric Energy Consumption forecasting (MEC) of a smart building using Transfer Learning and Long Short-Term Memory (TLL), the so-called MEC-TLL framework. In this framework, we first employ a k-means clustering algorithm to cluster the daily load demand of many profiles in the training set. In this phase, we also perform <i>Silhouette</i> analysis to specify the optimal number of clusters for the experimental datasets. Next, this study develops the MEC training algorithm, which utilizes a cluster-based strategy for transfer learning the Long Short-Term Memory models to reduce the computational time. Finally, extensive experiments are conducted to compare the computational time and different performance metrics for multiple electric energy consumption forecasting on two smart buildings in South Korea. The experimental results indicate that our proposed approach is capable of economical overheads while achieving superior performances. Therefore, the proposed approach can be applied effectively for intelligent energy management in smart buildings. |
| first_indexed | 2024-03-10T19:59:34Z |
| format | Article |
| id | doaj.art-945a171d0ec54029bb7c755938777efb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T19:59:34Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-945a171d0ec54029bb7c755938777efb2023-11-19T23:43:40ZengMDPI AGSensors1424-82202020-05-01209266810.3390/s20092668Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart BuildingTuong Le0Minh Thanh Vo1Tung Kieu2Eenjun Hwang3Seungmin Rho4Sung Wook Baik5Informetrics Research Group, Ton Duc Thang University, Ho Chi Minh City 700000, VietnamInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamUniversity of Science, Vietnam National University, Ho Chi Minh City 700000, VietnamSchool of Electrical Engineering, Korea University, Seoul 02841, KoreaDepartment of Software, Sejong University, Seoul 05006, KoreaDepartment of Software, Sejong University, Seoul 05006, KoreaElectric energy consumption forecasting is an interesting, challenging, and important issue in energy management and equipment efficiency improvement. Existing approaches are predictive models that have the ability to predict for a specific profile, i.e., a time series of a whole building or an individual household in a smart building. In practice, there are many profiles in each smart building, which leads to time-consuming and expensive system resources. Therefore, this study develops a robust framework for the Multiple Electric Energy Consumption forecasting (MEC) of a smart building using Transfer Learning and Long Short-Term Memory (TLL), the so-called MEC-TLL framework. In this framework, we first employ a k-means clustering algorithm to cluster the daily load demand of many profiles in the training set. In this phase, we also perform <i>Silhouette</i> analysis to specify the optimal number of clusters for the experimental datasets. Next, this study develops the MEC training algorithm, which utilizes a cluster-based strategy for transfer learning the Long Short-Term Memory models to reduce the computational time. Finally, extensive experiments are conducted to compare the computational time and different performance metrics for multiple electric energy consumption forecasting on two smart buildings in South Korea. The experimental results indicate that our proposed approach is capable of economical overheads while achieving superior performances. Therefore, the proposed approach can be applied effectively for intelligent energy management in smart buildings.https://www.mdpi.com/1424-8220/20/9/2668multiple electric energy consumption forecastinglong short-term memory networkstransfer learningthe cluster-based strategy for transfer learningintelligent energy management system |
| spellingShingle | Tuong Le Minh Thanh Vo Tung Kieu Eenjun Hwang Seungmin Rho Sung Wook Baik Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building Sensors multiple electric energy consumption forecasting long short-term memory networks transfer learning the cluster-based strategy for transfer learning intelligent energy management system |
| title | Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building |
| title_full | Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building |
| title_fullStr | Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building |
| title_full_unstemmed | Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building |
| title_short | Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building |
| title_sort | multiple electric energy consumption forecasting using a cluster based strategy for transfer learning in smart building |
| topic | multiple electric energy consumption forecasting long short-term memory networks transfer learning the cluster-based strategy for transfer learning intelligent energy management system |
| url | https://www.mdpi.com/1424-8220/20/9/2668 |
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