A Review of Energy Consumption Forecasting in Smart Buildings: Methods, Input Variables, Forecasting Horizon and Metrics
Buildings are among the largest energy consumers in the world. As new technologies have been developed, great advances have been made in buildings, turning conventional buildings into smart buildings. These smart buildings have allowed for greater supervision and control of the energy resources with...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/23/8323 |
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| author | Deyslen Mariano-Hernández Luis Hernández-Callejo Felix Santos García Oscar Duque-Perez Angel L. Zorita-Lamadrid |
| author_facet | Deyslen Mariano-Hernández Luis Hernández-Callejo Felix Santos García Oscar Duque-Perez Angel L. Zorita-Lamadrid |
| author_sort | Deyslen Mariano-Hernández |
| collection | DOAJ |
| description | Buildings are among the largest energy consumers in the world. As new technologies have been developed, great advances have been made in buildings, turning conventional buildings into smart buildings. These smart buildings have allowed for greater supervision and control of the energy resources within the buildings, taking steps to energy management strategies to achieve significant energy savings. The forecast of energy consumption in buildings has been a very important element in these energy strategies since it allows adjusting the operation of buildings so that energy can be used more efficiently. This paper presents a review of energy consumption forecasting in smart buildings for improving energy efficiency. Different forecasting methods are studied in nonresidential and residential buildings. Following this, the literature is analyzed in terms of forecasting objectives, input variables, forecasting methods and prediction horizon. In conclusion, the paper examines future challenges for building energy consumption forecasting. |
| first_indexed | 2024-03-10T14:37:08Z |
| format | Article |
| id | doaj.art-9d113e60da2d488598a677e2f71055ce |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T14:37:08Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9d113e60da2d488598a677e2f71055ce2023-11-20T22:07:14ZengMDPI AGApplied Sciences2076-34172020-11-011023832310.3390/app10238323A Review of Energy Consumption Forecasting in Smart Buildings: Methods, Input Variables, Forecasting Horizon and MetricsDeyslen Mariano-Hernández0Luis Hernández-Callejo1Felix Santos García2Oscar Duque-Perez3Angel L. Zorita-Lamadrid4Área de Ingeniería, Instituto Tecnológico de Santo Domingo, Santo Domingo 342-9, Dominican RepublicDepartamento Ingeniería Agrícola y Forestal, Universidad de Valladolid, 42005 Soria, SpainÁrea de Ciencias Básicas, Instituto Tecnológico de Santo Domingo, Santo Domingo 342-9, Dominican RepublicDepartamento de Ingeniería Eléctrica, Universidad de Valladolid, 47011 Valladolid, SpainDepartamento de Ingeniería Eléctrica, Universidad de Valladolid, 47011 Valladolid, SpainBuildings are among the largest energy consumers in the world. As new technologies have been developed, great advances have been made in buildings, turning conventional buildings into smart buildings. These smart buildings have allowed for greater supervision and control of the energy resources within the buildings, taking steps to energy management strategies to achieve significant energy savings. The forecast of energy consumption in buildings has been a very important element in these energy strategies since it allows adjusting the operation of buildings so that energy can be used more efficiently. This paper presents a review of energy consumption forecasting in smart buildings for improving energy efficiency. Different forecasting methods are studied in nonresidential and residential buildings. Following this, the literature is analyzed in terms of forecasting objectives, input variables, forecasting methods and prediction horizon. In conclusion, the paper examines future challenges for building energy consumption forecasting.https://www.mdpi.com/2076-3417/10/23/8323building energy consumptionforecasting methodsenergy forecastsmart building |
| spellingShingle | Deyslen Mariano-Hernández Luis Hernández-Callejo Felix Santos García Oscar Duque-Perez Angel L. Zorita-Lamadrid A Review of Energy Consumption Forecasting in Smart Buildings: Methods, Input Variables, Forecasting Horizon and Metrics Applied Sciences building energy consumption forecasting methods energy forecast smart building |
| title | A Review of Energy Consumption Forecasting in Smart Buildings: Methods, Input Variables, Forecasting Horizon and Metrics |
| title_full | A Review of Energy Consumption Forecasting in Smart Buildings: Methods, Input Variables, Forecasting Horizon and Metrics |
| title_fullStr | A Review of Energy Consumption Forecasting in Smart Buildings: Methods, Input Variables, Forecasting Horizon and Metrics |
| title_full_unstemmed | A Review of Energy Consumption Forecasting in Smart Buildings: Methods, Input Variables, Forecasting Horizon and Metrics |
| title_short | A Review of Energy Consumption Forecasting in Smart Buildings: Methods, Input Variables, Forecasting Horizon and Metrics |
| title_sort | review of energy consumption forecasting in smart buildings methods input variables forecasting horizon and metrics |
| topic | building energy consumption forecasting methods energy forecast smart building |
| url | https://www.mdpi.com/2076-3417/10/23/8323 |
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