Multi-step building energy model calibration process based on measured data

Building energy models are a key element in regulatory compliance calculations. These energy performance calculations often do not accurately reflect actual operating conditions. Therefore, evalua- tion of energy performance comparing actual energy use of a building with the outcome of dynamic sim- ulation models can be misleading, this difference is also known as the energy performance gap. The reduction of the gap is an important task aimed to provide confidence in the use of models for evaluation of energy efficiency. This paper is focused on reducing the technical issues (e.g. poorly adjusted thermal parameters in the envelope, inefficient boiler operator or lack of adjustment in parameters of heat pumps, baseboard radiators or air handling units) which are one of the main causes of the energy performance gap. The application of a multi-step, optimization-based, calibration methodology performed in a white-box simulation environment (EnergyPlus) using three months of ten minute time-step data to adjust HVAC parameter values with a genetic algorithm software (Jeplus) is validated on a real test site. Resulting in a BEM that fits the building’s hourly performance benchmark into international standards on three key levels: indoor temperature by Thermal Zone (TZ), heat production and electric consumption from heat pumps, which comprise all the components of a building energy model. A batch of 1500 h of heating operation, obtained from the building management system, has been used to calibrate the model. The results complied with the requirements of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Guideline 14–2002 at hourly interval, with NMBE 6–10%,Cv (RMSE) 630% and R2 P75% and with the International Performance Measurement and Verification Protocol (EVO) for Cv(RMSE) 620% and R2 P75% in the three aforementioned levels, which can be con- sidered a step forward in the area of calibrating white box models. In addition, to prove the strength and robustness of the results, the model has been checked in a long testing and independent period of 2.500 h of heating operations with the same level of compliance. The demonstrator is the library of a school located in Denmark. The HVAC system is composed of four air–water heat pumps that deliver heating to the whole compound with the backup support of a gas boiler. The library is heated with baseboard radiators system with the support of an air handling unit used for ventilation purposes

Towards a new generation of building envelope calibration

Building energy performance (BEP) is an ongoing point of reflection among researchers and practitioners. The importance of buildings as one of the largest activators in climate change mitigation was illustrated recently at the United Nations Framework Convention on Climate Change 21st Conference of the Parties (COP21). Continuous technological improvements make it necessary to revise the methodology for energy calculations in buildings, as has recently happened with the new international standard ISO 52016-1 on Energy Performance of Buildings. In this area, there is a growing need for advanced tools like building energy models (BEMs). BEMs should play an important role in this process, but until now there has no been international consensus on how these models should reconcile the gap between measurement and simulated data in order to make them more reliable and affordable. Our proposal is a new generation of models that reconcile the traditional data-driven (inverse) modelling and law-driven (forward) modelling in a single type that we have called law-data-driven models. This achievement has greatly simpli¿ed past methodologies, and is a step forward in the search for a standard in the process of calibrating a building energy model

Validation of calibrated energy models: Common errors

Nowadays, there is growing interest in all the smart technologies that provide us with information and knowledge about the human environment. In the energy ¿eld, thanks to the amount of data received from smart meters and devices and the progress made in both energy software and computers, the quality of energy models is gradually improving and, hence, also the suitability of Energy Conservation Measures (ECMs). For this reason, the measurement of the accuracy of building energy models is an important task, because once the model is validated through a calibration procedure, it can be used, for example, to apply and study different strategies to reduce its energy consumption in maintaining human comfort. There are several agencies that have developed guidelines and methodologies to establish a measure of the accuracy of these models, and the most widely recognized are: ASHRAE Guideline 14-2014, the International Performance Measurement and Veri¿cation Protocol (IPMVP) and the Federal Energy Management Program (FEMP). This article intends to shed light on these validation measurements (uncertainty indices) by focusing on the typical mistakes made, as these errors could produce a false belief that the models used are calibrated

Probabilistic load forecasting for building energy models

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

Ground characterization of building energy models

The calibration of building energy models is crucial for their use in some applications that depend on their accuracy for adequate performance, such as demand response and model predictive control (MPC). In general, energy models offer many possibilities/strategies when characterizing a construction system, and such a characterization is key when analyzing both its thermal behavior and its energy impact. This research analyzes the different ways to characterize the thermal interaction of the building energy model (BEM) with the ground, comparing conventional approaches with new approaches based on both optimization of the former and dynamic ground characterizations. Using a model adjusted to a real case study, each of the existing options are analyzed, in which a different control of the ground temperature both in terms of its temporal oscillation and its location in the building (based on thermal zones) is taken into account. Exhaustive monitoring of a real building and measuring the ground and ground floor surface temperatures have made establishing which EnergyPlus components/objects best characterize the ground-slab interaction possible, both in terms of the simplicity of modeling and the cost (economic and technical) required for each of them. As will be seen, there are objects with an excellent cost/effectiveness ratio when characterizing the groun

Nowcasting methods for optimising building performance

In meteorology term, nowcasting is weather forecasting for the next few minutes to six hours using all immediately available weather data. It is a relatively new subject, which often involves remote sensing, numerical weather prediction models, and advanced data communication infrastructure. High-quality weather nowcasting is crucial for optimising building performance in the near future. A range of nowcasting techniques has been used for such purposes. It includes statistical, machine learning, Numerical Weather Prediction (NWP), top-down and bottom-up approaches. This paper firstly reviews the advantages and disadvantages of common nowcasting methods with the focus on solar radiation nowcasting. Based on the review, popular methods have been classified into five categories. Authors then investigated further the nowcasting data provided by weather Application Programming Interfaces (APIs) that is backed by Numerical Weather Prediction. This is due to its large-scale application potential and the significances in the most recent update on solar radiation nowcast. Secondly, the paper explores the implications of applying weather nowcasting to dynamic building simulations, most importantly, examining its impact on the accuracy of indoor temperature prediction for free float buildings, heating load prediction and heating energy for heated buildings. The study used three buildings from BESTEST ANSI/ASHRAE Standard 140-2014 as the case studies. The results show that the most recent update of weather API includes meaningful solar radiation prediction. If the building does not have a large south facing glazing, the indoor temperature and heating load predictions from dynamic models are reasonably accurate

Evaluating the child-robot interaction of the NAOTherapist platform in pediatric rehabilitation

NAOTherapist is a cognitive robotic architecture whose main goal is to develop non-contact upper-limb rehabilitation sessions autonomously with a social robot for patients with physical impairments. In order to achieve a fluent interaction and an active engagement with the patients, the system should be able to adapt by itself in accordance with the perceived environment. In this paper, we describe the interaction mechanisms that are necessary to supervise and help the patient to carry out the prescribed exercises correctly. We also provide an evaluation focused on the child-robot interaction of the robotic platform with a large number of schoolchildren and the experience of a first contact with three pediatric rehabilitation patients. The results presented are obtained through questionnaires, video analysis and system logs, and have proven to be consistent with the hypotheses proposed in this work

Goal-directed Generation of Exercise Sets for Upper-Limb Rehabilitation

The proceeding at: 24th International Conference on Automated Planning and Scheduling. Portmouth, USA. June 21-26, 2014.WS 5: Knowledge Engineering for Planning and Scheduling (ICAPS KEPS-2014).A rehabilitation therapy usually derives from general goals set by the medical expert, who requests the patient to attend sessions during a certain time period in order to help him regaining mobility, strength and/or flexibility. The therapist must transform these general goals manually into a set of exercises distributed over different rehabilitation sessions that compose the complete therapy plan, taking into account the patient clinical conditions and a predetermined session and therapytime. This becomes a hard task and might lead to rigid schedules which not always accomplish the desired achievement level of therapeutic objectives established by the physician and could have a negative impact on the patients' engagement in the therapy. Classical and Hierarchical Task Network planning approaches have been used in this paper to compare the modellingand results of both domain formulations for the automatic generation of therapy plans for patients suffering obstetric rachial plexus palsy, in response to a given set of therapeutic objectives.This work has been partially granted by the Spanish Ministerio de Economía y Competitividad (MINECO) funds under coordinated project no. TIN2012-38079-C03-01, TIN2012- 38079-C03-02 and TIN2012-38079-C03-03, and FEDER INNTERCONECTA ADAPTA 2012. We also are grateful to the medical team of the Virgen del Rocío University Hospital for their participation.Publicad

Development of a REST API for obtaining site-specific historical and near-future weather data in EPW format

Obtaining site-specific high accuracy historical and near-future weather data have always been a challenging task for building simulation community to do either building performance analysis or predictive building control. Although ‘typical’ (such as TRY/TMY) or ‘extreme’ (such as DSY/DRY/EWF) weather files are made available, they often do not fit the purpose of studies. This paper demonstrates a novel approach to obtain real-time current and forecast weather in EPW format for building simulation using the free online toolchain. It is the first attempt to create weather API (Application programming interface) designed explicitly for building simulation community