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This paper investigates factors associated with variation in daily total (electricity and gas) energy consumption in domestic buildings using linked pre-COVID-19 smart meter, weather, building thermal characteristics, and socio-technical survey data covering appliance ownership, demographics, behaviours, and attitudes for two nested sub-samples of 1418 and 682 British households selected from the Smart Energy Research Laboratory (SERL) Observatory panel. Linear mixed effects modelling resulted in adjusted R2 between 63% and 80% depending on sample size and combinations of contextual data used. Increased daily energy consumption was significantly associated (p -value < 0.05, VIF < 5) with: households living in buildings with more rooms and bedrooms, that are older, more detached, have air-conditioning, and experience colder (more heating degree days) or less sunny weather; households with more adult occupants, more children, older adult occupants, higher heating temperature setpoints, and that do not try to save energy. The results demonstrate the value of smart meter data linked with contextual data for improving understanding of energy demand in British housing. Accredited UK researchers are invited to apply to access the data, which has recently been updated to include over 13,000 households from across Great Britain. This paper provides guidance on appropriate methods to use when analysing the data. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for an optimal control of the heating system at the building scale. This control is a new approach of energy planning that aims to decrease the heating consumption/expenses over a defined prediction horizon while respecting the occupants’ thermal comfort. It employs a simplified building thermal model to simulate the building thermal behavior taking into consideration the weather predictions. This approach is based on the application of Monte Carlo method, i.e., a random generator for the heating system scenarios. The aim is to determine the optimal heating plan for the prediction horizon that fulfills the constraints regarding the thermal comfort of occupants and the minimization of the energy consumption/expenses together with achieving a load shedding. [ABSTRACT FROM AUTHOR]

Abstract: The paper presents an extended method for exergy analysis of buildings and Heating Ventilation Air Conditioning (HVAC) systems, according to an energy demand build-up model from the building side to the energy supply side. The HVAC systems comprise a thermal energy emission and control system, a thermal distribution system, an electricity distribution system and an energy conversion system. Energy and exergy that are required by a building and a HVAC system are posed into the external part and classified by different forms of energy carriers. The external part is out of the boundary of the study. The method is illustrated with an office building equipped with low-temperature heating and high-temperature cooling systems situated in the Netherlands. Thermal exergy and thermal energy demands of the building and thermal energy and thermal exergy losses occurring in the HVAC systems are discussed. The building and the HVAC systems to be considered meet standard Dutch energy performance regulations. Nevertheless their overall exergy efficiencies are low in both cases (17.15% and 6.81% subsequently). The exergy analysis also pinpoints that the thermal energy emission and control system and the energy conversion system are the main causes of the exergy inefficiencies in the heating and cooling cases, respectively. [Copyright &y& Elsevier]

Abstract: Trigeneration systems, also known as Combined Heat, Cooling and Power (CHCP) systems, are interesting alternatives to supply different energy services in urban districts and in large buildings, particularly in warm areas such as Mediterranean countries. These systems can provide substantial benefits from economic, energetic, and environmental viewpoints, since the cogenerated heat can be used for heating in winter as well as cooling in summer with an absorption refrigerator. This paper develops an optimization model using Mixed Integer Linear Programming (MILP) to determine the type, number and capacity of equipment in CHCP systems installed in the tertiary sector as well as to establish the optimal operation mode for the different plant components on an hour-by-hour basis throughout the year. The objective function to be minimized is the annual total cost. The optimization model considers the legal constraints imposed to feed the surplus autogenerated electricity into the grid at a regulated feed-in tariff. The optimization model is applied to design a system providing energy services for a hospital located in the city of Zaragoza (Spain). The effects of the financial market conditions and energy prices in the optimal structure of the system are analyzed. [Copyright &y& Elsevier]