Uncertainty Analysis of Weather Forecast Data for Cooling Load Forecasting Based on the Monte Carlo Method

Zhao, Jing; Duan, Yaoqi; Liu, Xiaojuan

doi:10.3390/en11071900

Cited by 28 publications

(12 citation statements)

References 32 publications

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“…Regarding the load forecasts, Agüera et al [ 35 ] recommended the introduction of at least outdoor temperature forecasts, and they considered humidity estimations to be valuable. Studies that have accounted for weather forecasts have usually incorporated only outdoor temperature [ 52 ], outdoor temperature and relative humidity [ 34 , 36 ] or temperature and solar irradiation [ 32 ] but not all influential weather parameters. Previous studies from the authors have shown that weather parameters such as wind speed can be very influential on the building’s load [ 70 ].…”

Section: Discussionmentioning

confidence: 99%

“…The literature recognizes the significant influence of the weather forecast on a building’s energy performance, especially outdoor temperature [ 32 ]. However, the impact of the uncertainty due to forecast weather data on building load forecasting is not well represented in the literature [ 33 , 34 , 35 ], and few studies have directly investigated its effect [ 31 , 32 , 36 , 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

“…Dahl et al presented an autoregressive heat forecast model with weather prediction input and concluded that ensemble weather predictions could improve supply temperature control in district heating area substations [ 49 ]. Zhao et al used the Monte Carlo method (MCM) to pre-process meteorological forecast data to improve the accuracy of load forecasts provided by a support vector machine (SVM) model, and the forecasting results became closer to the actual data [ 34 ]. Similarly, Fan et al also employed the MCM to calibrate the input variables of their proposed SVM cooling load prediction model with the aim of reducing the influence of the uncertainty of the input variables (weather parameters, among others).…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic Load Forecasting for Building Energy Models

Segarra

Ruiz

Bandera

2020

Sensors

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In the current energy context of intelligent buildings and smart grids, the use of load forecasting to predict future building energy performance is becoming increasingly relevant. The prediction accuracy is directly influenced by input uncertainties such as the weather forecast, and its impact must be considered. Traditional load forecasting provides a single expected value for the predicted load and cannot properly incorporate the effect of these uncertainties. This research presents a methodology that calculates the probabilistic load forecast while accounting for the inherent uncertainty in forecast weather data. In the recent years, the probabilistic load forecasting approach has increased in importance in the literature but it is mostly focused on black-box models which do not allow performance evaluation of specific components of envelope, HVAC systems, etc. This research fills this gap using a white-box model, a building energy model (BEM) developed in EnergyPlus, to provide the probabilistic load forecast. Through a Gaussian kernel density estimation (KDE), the procedure converts the point load forecast provided by the BEM into a probabilistic load forecast based on historical data, which is provided by the building’s indoor and outdoor monitoring system. An hourly map of the uncertainty of the load forecast due to the weather forecast is generated with different prediction intervals. The map provides an overview of different prediction intervals for each hour, along with the probability that the load forecast error is less than a certain value. This map can then be applied to the forecast load that is provided by the BEM by applying the prediction intervals with their associated probabilities to its outputs. The methodology was implemented and evaluated in a real school building in Denmark. The results show that the percentage of the real values that are covered by the prediction intervals for the testing month is greater than the confidence level (80%), even when a small amount of data are used for the creation of the uncertainty map; therefore, the proposed method is appropriate for predicting the probabilistic expected error in load forecasting due to the use of weather forecast data.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Probabilistic Load Forecasting for Building Energy Models

Segarra

Ruiz

Bandera

2020

Sensors

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“…Comparing the energy demand between the real weather simulation and the simulations with each changed parameters through the mean (µ) and the standard deviation (σ), the significance of each weather parameter is obtained. Figure 7 shows, for this period of study, that the outdoor temperature is the most sensitive weather parameter and has the greatest impact on the energy demand, as other studies have concluded [25,52]. For this reason, in the following analysis, only the outdoor temperature will be used.…”

Section: Sensitivity Analysis Of Weather Parametersmentioning

confidence: 99%

Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models

Segarra

Ruiz

et al. 2019

Energies

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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.

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“…There are many studies that highlight the importance of the weather files, measuring, for example, their impact on passive buildings [34], on micro-grids [35,36], etc., calculating the loads of district energy systems [37], the electricity consumption with demand response strategies [13], or evaluating the effect on comfort conditions [38]. Some analyzed the effect that certain parameters of the weather file have, emphasizing the temperature as the most sensitive value for load forecasting [39,40].…”

Section: Introductionmentioning

confidence: 99%

Impact Assessment for Building Energy Models Using Observed vs. Third-Party Weather Data Sets

et al. 2020

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The use of building energy models (BEMs) is becoming increasingly widespread for assessing the suitability of energy strategies in building environments. The accuracy of the results depends not only on the fit of the energy model used, but also on the required external files, and the weather file is one of the most important. One of the sources for obtaining meteorological data for a certain period of time is through an on-site weather station; however, this is not always available due to the high costs and maintenance. This paper shows a methodology to analyze the impact on the simulation results when using an on-site weather station and the weather data calculated by a third-party provider with the purpose of studying if the data provided by the third-party can be used instead of the measured weather data. The methodology consists of three comparison analyses: weather data, energy demand, and indoor temperature. It is applied to four actual test sites located in three different locations. The energy study is analyzed at six different temporal resolutions in order to quantify how the variation in the energy demand increases as the time resolution decreases. The results showed differences up to 38% between annual and hourly time resolutions. Thanks to a sensitivity analysis, the influence of each weather parameter on the energy demand is studied, and which sensors are worth installing in an on-site weather station are determined. In these test sites, the wind speed and outdoor temperature were the most influential weather parameters.

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Uncertainty Analysis of Weather Forecast Data for Cooling Load Forecasting Based on the Monte Carlo Method

Cited by 28 publications

References 32 publications

Probabilistic Load Forecasting for Building Energy Models

Probabilistic Load Forecasting for Building Energy Models

Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models

Impact Assessment for Building Energy Models Using Observed vs. Third-Party Weather Data Sets

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