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

Segarra, Eva Lucas; Ruiz, Germán Ramos; González, Vicente Gutiérrez; Peppas, Athanasios; Bandera, Carlos Fernández

doi:10.3390/su12176788

Cited by 11 publications

(4 citation statements)

References 48 publications

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“…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 ]. For this reason, this research introduced forecast data for outdoor temperature, relative humidity, direct normal irradiation, diffuse horizontal irradiation, wind speed and wind direction.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

Probabilistic Load Forecasting for Building Energy Models

Segarra

Ruiz

Bandera

2020

Sensors

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“…There are challenges in the calibration process for the measurement and verification of energy savings, which can be based on mathematical algorithms and physical-based models and are evaluated according to uncertainty analysis [5][6][7]. As stated by the ASHRAE Handbook: Fundamentals: SI edition (2017) [8], several difficulties prevent achieving a calibrated simulation [9][10][11][12]. One of them is the method used to measure the input parameters needed for the simulation, i.e., infiltration values [13][14][15][16].…”

Section: Background and Motivationmentioning

confidence: 99%

A Case Study of Empirical Validation of EnergyPlus Infiltration Models Based on Different Wind Data

Porsani

Bandera

2023

Buildings

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Building retrofitting is an efficient means of reducing greenhouse gas emissions. Its first focus is on building façade, as transmission and air leakage are the main sources of energy loss in buildings. Nowadays, building modellers cannot easily implement envelope air leakage and assume constant values, which results in erroneous energy estimates. Additionally, in energy simulations, a weather file is usually inserted with measurements provided by a weather station. In this study, we revealed the use of wind data from the weather file (herein as global wind) to calculate the infiltration of a test case in Spain, using the three algebraic equations of EnergyPlus. Furthermore, four other wind data were applied: eastbound and westbound winds from the weather file and two from in situ measurements (on the southeast and on the northwest façades). The fifteen combinations of the three infiltration models and the five wind data were empirically evaluated, using the tracer gas results performed during three different periods. The combinations were validated according to the American Society for Testing Materials D5157 standard criteria, and the best and the only ones that complied with the standard were those using the wind data from the southeast in situ sensor and the west wind from the weather station. The global wind was not able to generate accurate infiltration models, which raises doubts about its use in the highly-time calibration of energy models. However, its disaggregation was a cost-effective strategy to estimate the infiltration of this case study.

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“…Specific calibration techniques for building envelope calibration can be consulted in references (Ramos Ruiz et al 2016;Fernández Bandera and Ramos Ruiz 2017;Gutiérrez González et al 2020) and for heating ventilation and air conditioning systems (HVAC systems) in references (Pachano and Fernández Bandera 2021;Pachano et al 2022). The error for the weather has been evaluated in the following studies (Lucas Segarra et al 2020b;Gutiérrez González et al 2021) and for the weather forecast in references (Lucas Segarra et al 2019, 2020a.…”

Section: The Concept: Demand Side Managementmentioning

confidence: 99%

A demand side management approach to increase self-consumption in buildings

2022

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There is a growing interest in increasing the presence of renewable energy in the electric network. Photovoltaic production from grid-connected systems is leading this growth in terms of households. Alongside this development, concern about network security has emerged, because excesses of intermittent renewable energy on the grid could exceed voltage limits. Self-consumption, understood as the capacity of the producer to consume his or her own production, can partially solve these problems. Thermostatic controllable loads, such as heating and cooling, represent 50% of the total amount of energy consumed by buildings; the proper allocation of these loads could be a driving force for self-consumption. In this study, a demand side management strategy is proposed based on a building energy model equipped with an inverter heat pump coupled with a photovoltaic plant. The goal is to maximize the use of local energy from the photovoltaic plant (self-consumption), reducing the export and import of energy to and from the grid. This goal is achieved by optimizing the set-points in each room. An array of optimal set-points over six years is presented. The results show the capacity of the methodology to match similar values of self-consumption (70% in winter and 50% in summer) obtained by strategies based on chemical batteries. The findings are shown in an energy matching chart at different levels of detail (yearly and monthly). Color bubbles are added to the matching chart to help visualize the unmatched energy of the system graphically. In comparison with actual model predictive control technologies, this study’s strategy offers great simplicity and a large saving in computational time.

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

Cited by 11 publications

References 48 publications

Probabilistic Load Forecasting for Building Energy Models

Probabilistic Load Forecasting for Building Energy Models

A Case Study of Empirical Validation of EnergyPlus Infiltration Models Based on Different Wind Data

A demand side management approach to increase self-consumption in buildings

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