Cargando…
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...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7362249/ https://www.ncbi.nlm.nih.gov/pubmed/32392858 http://dx.doi.org/10.3390/s20092668 |
_version_ | 1783559466839441408 |
---|---|
author | Le, Tuong Vo, Minh Thanh Kieu, Tung Hwang, Eenjun Rho, Seungmin Baik, Sung Wook |
author_facet | Le, Tuong Vo, Minh Thanh Kieu, Tung Hwang, Eenjun Rho, Seungmin Baik, Sung Wook |
author_sort | Le, Tuong |
collection | PubMed |
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 Silhouette 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. |
format | Online Article Text |
id | pubmed-7362249 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-73622492020-07-21 Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building Le, Tuong Vo, Minh Thanh Kieu, Tung Hwang, Eenjun Rho, Seungmin Baik, Sung Wook Sensors (Basel) Article 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 Silhouette 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. MDPI 2020-05-07 /pmc/articles/PMC7362249/ /pubmed/32392858 http://dx.doi.org/10.3390/s20092668 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Le, Tuong Vo, Minh Thanh Kieu, Tung Hwang, Eenjun Rho, Seungmin Baik, Sung Wook Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building |
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 | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7362249/ https://www.ncbi.nlm.nih.gov/pubmed/32392858 http://dx.doi.org/10.3390/s20092668 |
work_keys_str_mv | AT letuong multipleelectricenergyconsumptionforecastingusingaclusterbasedstrategyfortransferlearninginsmartbuilding AT vominhthanh multipleelectricenergyconsumptionforecastingusingaclusterbasedstrategyfortransferlearninginsmartbuilding AT kieutung multipleelectricenergyconsumptionforecastingusingaclusterbasedstrategyfortransferlearninginsmartbuilding AT hwangeenjun multipleelectricenergyconsumptionforecastingusingaclusterbasedstrategyfortransferlearninginsmartbuilding AT rhoseungmin multipleelectricenergyconsumptionforecastingusingaclusterbasedstrategyfortransferlearninginsmartbuilding AT baiksungwook multipleelectricenergyconsumptionforecastingusingaclusterbasedstrategyfortransferlearninginsmartbuilding |