Empirical and Comparative Validation for a Building Energy Model Calibration Methodology

González, Vicente Gutiérrez; Ruiz, Germán Ramos; Bandera, Carlos Fernández

doi:10.3390/s20175003

Cited by 32 publications

(16 citation statements)

References 56 publications

(60 reference statements)

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“…On The research method used started with the selection of the weather files. Once they had been selected, the process of calibrating the base model began [9]. The objective of this procedure was to justify the impact that the weather files had on the calibrated model, thus checking which was the energy model that together with its weather file best fit reality and to verify that the ranking generated in the simulations of the base model with all the weather files was fulfilled ( Figure 2).…”

Section: Weather Combination Weather Combination Weather Filementioning

confidence: 99%

“…Energy prediction relies entirely on models, and therefore one of the main pillars of SABINA is the production of high-quality models (calibrated) that can give reliability to P2H technology. These models are constructed on the basis on an initial methodology developed by Ramos et al [7] and Bandera et al [8], which has been recently improved and empirically validated by Gutiérrez et al [9] based on the work carried out by Annex 58 of the Committee of the International Energy Agency Energy in Buildings and Communities program (IEA-EBC) approved in 2011 and completed in 2016. The main objectives of Annex 58 were: to develop common quality procedures for dynamic full-scale testing to come to a better performance analysis and develop models to characterize and predict the effective thermal performance of building components and whole buildings [10].…”

Section: Introductionmentioning

confidence: 99%

“…Calibration environment with a genetic algorithm. The coefficient of variation of the root mean square error (CV(RMSE)) and the genetic algorithm non-dominated sorting genetic algorithm (NSGA-II)[9].…”

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confidence: 99%

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Impact of Actual Weather Datasets for Calibrating White-Box Building Energy Models Base on Monitored Data

2021

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The need to reduce energy consumption in buildings is an urgent task. Increasing the use of calibrated building energy models (BEM) could accelerate this need. The calibration process of these models is a highly under-determined problem that normally yields multiple solutions. Among the uncertainties of calibration, the weather file has a primary position. The objective of this paper is to provide a methodology for selecting the optimal weather file when an on-site weather station with local sensors is available and what is the alternative option when it is not and a mathematically evaluation has to be done with sensors from nearby stations (third-party providers). We provide a quality assessment of models based on the Coefficient of Variation of the Root Mean Square Error (CV(RMSE)) and the Square Pearson Correlation Coefficient (R2). The research was developed on a control experiment conducted by Annex 58 and a previous calibration study. This is based on the results obtained with the study case based on the data provided by their N2 house.

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Section: Weather Combination Weather Combination Weather Filementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impact of Actual Weather Datasets for Calibrating White-Box Building Energy Models Base on Monitored Data

2021

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“…In the load forecasting field, when using BEMs, it is important to take into account the three main sources of uncertainty: BEM accuracy, building use, and external conditions. This case study used a calibrated BEM, obtained using a calibration methodology explained in the authors' previous papers [59,[61][62][63][64]. Using a calibrated model allows minimizing the uncertainty due to BEM accuracy.…”

Section: Description Of the Case Studymentioning

confidence: 99%

Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization

Segarra

Ruiz

Bandera

2021

Sensors

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Accurate load forecasting in buildings plays an important role for grid operators, demand response aggregators, building energy managers, owners, customers, etc. Probabilistic load forecasting (PLF) becomes essential to understand and manage the building’s energy-saving potential. This research explains a methodology to optimize the results of a PLF using a daily characterization of the load forecast. The load forecast provided by a calibrated white-box model and a real weather forecast was classified and hierarchically selected to perform a kernel density estimation (KDE) using only similar days from the database characterized quantitatively and qualitatively. A real case study is presented to show the methodology using an office building located in Pamplona, Spain. The building monitoring, both inside—thermal sensors—and outside—weather station—is key when implementing this PLF optimization technique. The results showed that thanks to this daily characterization, it is possible to optimize the accuracy of the probabilistic load forecasting, reaching values close to 100% in some cases. In addition, the methodology explained is scalable and can be used in the initial stages of its implementation, improving the values obtained daily as the database increases with the information of each new day.

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“…In the load forecasting field, when using BEMs, it is necessary to take into account three main sources of uncertainty: BEM accuracy, building use and external conditions. In order to minimize the first uncertainty, this case study employed a calibrated BEM, obtained using a calibration methodology explained in the authors' previous papers [58,[65][66][67][68]. Regarding the building's use, no uncertainty was consider in the indoor conditions since the model used indoor temperatures measured in each thermal zone by the BMS.…”

Section: Description Of the Case Studymentioning

confidence: 99%

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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Empirical and Comparative Validation for a Building Energy Model Calibration Methodology

Cited by 32 publications

References 56 publications

Impact of Actual Weather Datasets for Calibrating White-Box Building Energy Models Base on Monitored Data

Impact of Actual Weather Datasets for Calibrating White-Box Building Energy Models Base on Monitored Data

Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization

Probabilistic Load Forecasting for Building Energy Models

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