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

Research priorities in observing and modeling urban weather and climate

In 2007, the world reached the unprecedented milestone of half of its people living in cities, and that proportion is projected to be 60% in 2030. The combined effect of global climate change and rapid urban growth, accompanied by economic and industrial development, will likely make city residents more vulnerable to a number of urban environmental problems, including extreme weather and climate conditions, sea-level rise, poor public health and air quality, atmospheric transport of accidental or intentional releases of toxic material, and limited water resources. One fundamental aspect of predicting the future risks and defining mitigation strategies is to understand the weather and regional climate affected by cities. For this reason, dozens of researchers from many disciplines and nations attended the Urban Weather and Climate Workshop.1 Twenty-five students from Chinese universities and institutes also took part. The presentations by the workshop's participants span a wide range of topics, from the interaction between the urban climate and energy consumption in climate-change environments to the impact of urban areas on storms and local circulations, and from the impact of urbanization on the hydrological cycle to air quality and weather prediction

Bayesian Calibration - What, Why And How

Calibration of building energy models is important to ensure accurate modeling of existing buildings. Typically this calibration is done manually by modeling experts, which can be both expensive and time consuming. Â Additionally, biases of the individual modelers will creep into the process. Â Many methods of automated calibration have been developed which reduce costs, time and biases, including optimization using genetic or swarming algorithms, machine learning methods, and Bayesian methods. Â Bayesian methods differ significantly from the other optimization and machine learning methods in that inputs are assumed to be uncertain and main goal is not to match the prediction to the measured data as closely as possible, but to reduce the uncertainty in the inputs in a manner consistent with the measured data. Â Bayesian methods are particularly useful when there are model inputs that have high sensitivity and high uncertainty and where there is limited measured data to use for calibration. In this paper, the basic concepts of Bayesian calibration are explained and a typical application and results are demonstrated

Reducing Simulation Performance Gap from Hempcrete Buildings Using Multi Objective Optimization

© International Building performance Simulation Association, 2020.Hempcrete is increasingly used as a construction material, as it provides stable temperature and relative humidity conditions in buildings. In addition to low energy operations, buildings built from hempcrete possess negative embodied CO2, absorbed into the hemp plant material. Hempcrete is hard to represent in design simulations because standard dynamic simulation tools do not have a built-in capability to simulate its effect accurately, due to the specific material structure and combined heat and moisture transfer, causing a considerable performance gap. This paper investigates appropriate specification of key parameters to be used in simulation of hempcrete, to reduce simulation performance gap from hempcrete buildings, using multi objective optimisation, to facilitate hempcrete simulation

Impact of Air-to-Air Heat Pumps on Energy and Climate in a Mid-Latitude City

Exploring the potential effects of transitioning entirely to air-to-air heat pumps (AAHPs), we use an integrated weather and heat pump model to understand their performance across several building and weather conditions in Toulouse, France. In central Toulouse, where electric and gas heating are similarly adopted, a shift to AAHPs cuts annual electric consumption. Yet, during colder periods, a drop in their efficiency can cause a spike in electricity use. In regions predominantly relying on non-electric heaters, such as gas boilers, introducing AAHPs is expected to increase electricity demand as the heating system transitions to all-electric, though to a lesser extent and with much greater efficiency than traditional systems such as electric resistive heaters. In a separate analysis to evaluate the impact of AAHPs on local climate conditions, we find that AAHPs have a small influence of about 0.5 {\deg}C on the outdoor air temperature. This change is thus unlikely to meaningfully alter AAHPs' performance through feedback.Comment: Submitted manuscrip

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

Artificial Intelligence Method for the Forecast and Separation of Total and HVAC Loads with Application to Energy Management of Smart and NZE Homes

Separating the HVAC energy use from the total residential load can be used to improve energy usage monitoring and to enhance the house energy management systems (HEMS) for existing houses that do not have dedicated HVAC circuits. In this paper, a novel method is proposed to separate the HVAC dominant load component from the house load. The proposed method utilizes deep learning techniques and the physical relationship between HVAC energy use and weather. It employs novel long short-term memory (LSTM) encoder-decoder machine learning (ML) models, which are developed based on future weather data input in place of weather forecasts. In addition to being used in the proposed HVAC separation method, the LSTM models are employed also for accurate day-ahead HVAC and solar photovoltaic (PV) energy forecasts. To test and validate the proposed method, the SHINES dataset, a publicly available dataset spanning three years at 15-minute time resolution from a large-scale DOE experimental project, is used. Two computational case studies are constructed with a test HEMS to investigate the power regulating capability of smart home virtual operation as dispatchable loads or generators. Prediction results obtained with the proposed method show hourly and daily CV(RMSE) of 29.4% and 11.1%, respectively. These results are well within the bounds of error established by academia and the ASHRAE building model and calibration standards

Exergy as a measure of sustainable retrofitting of buildings

This study presents a novel optimization methodology for choosing optimal building retrofitting strategies based on the concept of exergy analysis. The study demonstrates that the building exergy analysis may open new opportunities in the design of an optimal retrofit solution despite being a theoretical approach based on the high performance of a Carnot reverse cycle. This exergy-based solution is different from the one selected through traditional efficient retrofits where minimizing energy consumption is the primary selection criteria. The new solution connects the building with the reference environment, which acts as “an unlimited sink or unlimited sources of energy”, and it adapts the building to maximize the intake of energy resources from the reference environment. The building hosting the School of Architecture at the University of Navarra has been chosen as the case study building. The unique architectural appearance and bespoke architectural characteristics of the building limit the choices of retrofitting solutions; therefore, retrofitting solutions on the façade, roof, roof skylight and windows are considered in multi-objective optimization using the jEPlus package. It is remarkable that different retrofitting solutions have been obtained for energy-driven and exergy-driven optimization, respectively. Considering the local contexts and all possible reference environments for the building, three “unlimited sinks or unlimited sources of energy” are selected for the case study building to explore exergy-driven optimization: the external air, the ground in the surrounding area and the nearby river. The evidence shows that no matter which reference environment is chosen, an identical envelope retrofitting solution has been obtaine