Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models
The use of Building Energy Models (BEM) has become widespread to reduce building energy consumption. Projection of the model in the future to know how different consumption strategies can be evaluated is one of the main applications of BEM. Many energy management optimization strategies can be used...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/7/1309 |
| _version_ | 1811307154971295744 |
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| author | Eva Lucas Segarra Hu Du Germán Ramos Ruiz Carlos Fernández Bandera |
| author_facet | Eva Lucas Segarra Hu Du Germán Ramos Ruiz Carlos Fernández Bandera |
| author_sort | Eva Lucas Segarra |
| collection | DOAJ |
| description | The use of Building Energy Models (BEM) has become widespread to reduce building energy consumption. Projection of the model in the future to know how different consumption strategies can be evaluated is one of the main applications of BEM. Many energy management optimization strategies can be used and, among others, model predictive control (MPC) has become very popular nowadays. When using models for predicting the future, we have to assume certain errors that come from uncertainty parameters. One of these uncertainties is the weather forecast needed to predict the building behavior in the near future. This paper proposes a methodology for quantifying the impact of the error generated by the weather forecast in the building’s indoor climate conditions and energy demand. The objective is to estimate the error introduced by the weather forecast in the load forecasting to have more precise predicted data. The methodology employed site-specific, near-future forecast weather data obtained through online open access Application Programming Interfaces (APIs). The weather forecast providers supply forecasts up to 10 days ahead of key weather parameters such as outdoor temperature, relative humidity, wind speed and wind direction. This approach uses calibrated EnergyPlus models to foresee the errors in the indoor thermal behavior and energy demand caused by the increasing day-ahead weather forecasts. A case study investigated the impact of using up to 7-day weather forecasts on mean indoor temperature and energy demand predictions in a building located in Pamplona, Spain. The main novel concepts in this paper are: first, the characterization of the weather forecast error for a specific weather data provider and location and its effect in the building’s load prediction. The error is calculated based on recorded hourly data so the results are provided on an hourly basis, avoiding the cancel out effect when a wider period of time is analyzed. The second is the classification and analysis of the data hour-by-hour to provide an estimate error for each hour of the day generating a map of hourly errors. This application becomes necessary when the building takes part in the day-ahead programs such as demand response or flexibility strategies, where the predicted hourly load must be provided to the grid in advance. The methodology developed in this paper can be extrapolated to any weather forecast provider, location or building. |
| first_indexed | 2024-04-13T08:59:35Z |
| format | Article |
| id | doaj.art-13a5718cf7754c1c916a27b16ddb55bd |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T08:59:35Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-13a5718cf7754c1c916a27b16ddb55bd2022-12-22T02:53:11ZengMDPI AGEnergies1996-10732019-04-01127130910.3390/en12071309en12071309Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation ModelsEva Lucas Segarra0Hu Du1Germán Ramos Ruiz2Carlos Fernández Bandera3School of Architecture, University of Navarra, 31009 Pamplona, SpainWelsh School of Architecture, Cardiff University, Bute Building, King Edward VII Avenue, Cardiff, Wales CF10 3NB, UKSchool of Architecture, University of Navarra, 31009 Pamplona, SpainSchool of Architecture, University of Navarra, 31009 Pamplona, SpainThe use of Building Energy Models (BEM) has become widespread to reduce building energy consumption. Projection of the model in the future to know how different consumption strategies can be evaluated is one of the main applications of BEM. Many energy management optimization strategies can be used and, among others, model predictive control (MPC) has become very popular nowadays. When using models for predicting the future, we have to assume certain errors that come from uncertainty parameters. One of these uncertainties is the weather forecast needed to predict the building behavior in the near future. This paper proposes a methodology for quantifying the impact of the error generated by the weather forecast in the building’s indoor climate conditions and energy demand. The objective is to estimate the error introduced by the weather forecast in the load forecasting to have more precise predicted data. The methodology employed site-specific, near-future forecast weather data obtained through online open access Application Programming Interfaces (APIs). The weather forecast providers supply forecasts up to 10 days ahead of key weather parameters such as outdoor temperature, relative humidity, wind speed and wind direction. This approach uses calibrated EnergyPlus models to foresee the errors in the indoor thermal behavior and energy demand caused by the increasing day-ahead weather forecasts. A case study investigated the impact of using up to 7-day weather forecasts on mean indoor temperature and energy demand predictions in a building located in Pamplona, Spain. The main novel concepts in this paper are: first, the characterization of the weather forecast error for a specific weather data provider and location and its effect in the building’s load prediction. The error is calculated based on recorded hourly data so the results are provided on an hourly basis, avoiding the cancel out effect when a wider period of time is analyzed. The second is the classification and analysis of the data hour-by-hour to provide an estimate error for each hour of the day generating a map of hourly errors. This application becomes necessary when the building takes part in the day-ahead programs such as demand response or flexibility strategies, where the predicted hourly load must be provided to the grid in advance. The methodology developed in this paper can be extrapolated to any weather forecast provider, location or building.https://www.mdpi.com/1996-1073/12/7/1309weather forecast uncertaintybuilding energy modelbuilding simulationenergy flexible buildingsmodel predictive control |
| spellingShingle | Eva Lucas Segarra Hu Du Germán Ramos Ruiz Carlos Fernández Bandera Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models Energies weather forecast uncertainty building energy model building simulation energy flexible buildings model predictive control |
| title | Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models |
| title_full | Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models |
| title_fullStr | Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models |
| title_full_unstemmed | Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models |
| title_short | Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models |
| title_sort | methodology for the quantification of the impact of weather forecasts in predictive simulation models |
| topic | weather forecast uncertainty building energy model building simulation energy flexible buildings model predictive control |
| url | https://www.mdpi.com/1996-1073/12/7/1309 |
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