Evaluation of energy and indoor environmental performance of a UK passive house dwelling

The preliminary findings of the energy and indoor environmental performance of a Passive House dwelling in North East of England is presented in this paper. This dwelling is designed to comply with the Passive House Standard (certified by the International Passive House Association) which aims to reduce energy consumption and carbon emissions. The property benefits from advanced building fabric design and materials, PV array, mechanical ventilation with heat recovery system (MVHR) and high efficiency domestic hot water storage vessel to minimise operational carbon emissions. Power generated by the PV panel, imported grid electricity and mains gas consumption of this house are monitored by a proprietary monitoring package; and data of indoor temperature, relative humidity and resident occupancy at several different locations in the dwelling are also recorded. A computational model of this property was developed using DesignBuilder software. The model was validated using the data monitored on site; and is used to predict and evaluate the performance of the house. The initial findings of this study shows the advantages of Passive House in achieving high thermal comfort and good indoor air quality with much lower energy consumption compares to the national averag

A multi-zone, fast solving, rapidly reconfigurable building and electrified heating system model for generation of control dependent heat pump power demand profiles

The electrification of heating is expected to grow in the UK domestic sector, and this has increased interest in the effects that this may have on low and high voltage network operation. However, Electrified heating profiles that alter with control decisions can only be obtained from dedicated building modelling that energy system modellers do not usually have the expertise to perform, yet these are required for meaningful studies. This work outlines a novel method for modelling air source and ground source heat pump power demand profiles using a multi-zone physics based building modelling framework with building fabric, thermohydraulic, and air flow subsystems. The novel setup framework allows detailed building layout, fabric and control properties to be assigned by analysts with no prior building modelling expertise. Once fully assigned, the building model can be used to generate heat pump power demand profiles at sub minute resolution. Upon testing, a single daily run of the model could be executed in 17 s. The model was then validated against real life test house data, under various control and weather conditions. A small relative error (typically within 10%) was observed between modelled and actual cycle lengths, and modelled and actual heat and electricity demands. Due to its rapid solution rate, the model is of significant value to energy efficiency and distribution network studies, where large demand profile sets that are sensitive to detailed retrofit and control considerations are often essential. The model has been made openly available

Calibration under Uncertainty Using Bayesian Emulation and History Matching: Methods and Illustration on a Building Energy Model

\ua9 2024 by the authors.Energy models require accurate calibration to deliver reliable predictions. This study offers statistical guidance for a systematic treatment of uncertainty before and during model calibration. Statistical emulation and history matching are introduced. An energy model of a domestic property and a full year of observed data are used as a case study. Emulators, Bayesian surrogates of the energy model, are employed to provide statistical approximations of the energy model outputs and explore the input parameter space efficiently. The emulator’s predictions, alongside quantified uncertainties, are then used to rule out parameter configurations that cannot lead to a match with the observed data. The process is automated within an iterative procedure known as history matching (HM), in which simulated gas consumption and temperature data are simultaneously matched with observed values. The results show that only a small percentage of parameter configurations (0.3% when only gas consumption is matched, and 0.01% when both gas and temperature are matched) yielded outputs matching the observed data. This demonstrates HM’s effectiveness in pinpointing the precise region where model outputs align with observations. The proposed method is intended to offer analysts a robust solution to rapidly explore a model’s response across the entire input space, rule out regions where a match with observed data cannot be achieved, and account for uncertainty, enhancing the confidence in energy models and their viability as a decision support tool

Data for: Building as a Virtual Power Plant, Magnitude and Persistence of Deferrable Loads and Human Comfort Implications

Data in summary:1-Building total B side: This is metered data from one of two mains busbars that supplies all none-emergency services and HVAC equipment2-Building total A side: This is metered data from the second of two mains busbars that supplies all emergency services including fire safety, comm rooms, emergency lighting and public announcement. It also is connected to a PV array with peak electrical supply of around 33kWe.3-Half hourly building demand and deferrable load breakdowns: This is processed data that includes building total and HH instances of deferrable loads for all sub-categories of loads considered in this work. It also includes HH instances of PV generation, and outside air temperature.4-Early morning ramp rates following plant start-up: This is a file containing the difference between two instantaneous recordings of total building electricity consumption that shows the continuous fluctuation in total electricity demand in the building. 5-CO2-raw (Typical office): This files contains actual CO2 data in an office that represents typical space occupant density in the case study building.6-CO2-raw (worst case): This files contains actual CO2 data in a teaching space that represents the highest observed space occupant density in the case study building.7-Warming and cooling rates in the worst case zones: This file include actual data describing the operational temperature in the worst case zones most prone to overheating in summer and excessive heat loss in winter

Data for: Building as a Virtual Power Plant, Magnitude and Persistence of Deferrable Loads and Human Comfort Implications

Data in summary:1-Building total B side: This is metered data from one of two mains busbars that supplies all none-emergency services and HVAC equipment2-Building total A side: This is metered data from the second of two mains busbars that supplies all emergency services including fire safety, comm rooms, emergency lighting and public announcement. It also is connected to a PV array with peak electrical supply of around 33kWe.3-Half hourly building demand and deferrable load breakdowns: This is processed data that includes building total and HH instances of deferrable loads for all sub-categories of loads considered in this work. It also includes HH instances of PV generation, and outside air temperature.4-Early morning ramp rates following plant start-up: This is a file containing the difference between two instantaneous recordings of total building electricity consumption that shows the continuous fluctuation in total electricity demand in the building. 5-CO2-raw (Typical office): This files contains actual CO2 data in an office that represents typical space occupant density in the case study building.6-CO2-raw (worst case): This files contains actual CO2 data in a teaching space that represents the highest observed space occupant density in the case study building.7-Warming and cooling rates in the worst case zones: This file include actual data describing the operational temperature in the worst case zones most prone to overheating in summer and excessive heat loss in winter.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV