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Abstract This article introduces a parameter estimation and state prediction technique called constrained dual Extended Kalman Filter (EKF) that can be used in model prediction controls (MPC) and fault detection and diagnostics (FDD) in building systems. The proposed method is an improvement over the existing nonlinear filter-based methods such as the joint EKF or Unscented Kalman Filter, which is widely adopted in previous building controls research. Case studies using both simulation and real measurements are conducted to demonstrate the proposed algorithm when used for a building thermal zone. Data from parametric simulations are used to test the thermal model's ability to capture parameter variations. Measured data from five identical offices is collected to test the performance of parameter estimates and state predictions under real-life operation. Overall, the proposed algorithm is 25% faster than a conventional EKF with improved numerical stability. It can also help mitigate numerical instabilities seen in previous EKF research. The reduced thermal model used in this paper is also capable of detecting most of the parametric changes to the thermal zone and providing reliable temperature predictions. [ABSTRACT FROM AUTHOR]

Abstract: Nowadays, heating energy demand has become a significant estimator used during the design stage of any new building. In this paper we are proposing a model to predict the heating energy demand, based on the main factors that influence a building''s heat consumption. It was found out that these factors are: the building global heat loss coefficient (G), the south equivalent surface (SES) and the difference between the indoor set point temperature and the sol-air temperature. In the second part of this paper, multiple dynamic simulations were carried out in order to determine the values of the inputs and output data of the future prediction model. Using the obtained database, a multiple regression prediction model was further used to develop the prediction model. In the last part of this paper the model results was validated with the measured data from 17 blocks of flats. Moreover, in this article it is also shown the comparison with the results calculated using the building''s energy certification methodology. A detailed error analysis showed that the model presents a very good accuracy (correlation coefficient of 0.987). In conclusion, the proposed model presents the following characteristics: three inputs and one output, simplicity, large applicability, good match with the simulations and with the energy certification calculations, human behavior correction. [Copyright &y& Elsevier]

Building energy simulators such as TRNSYS, EnergyPlus, and Esp-r offer an excellent opportunity for detailed design of house thermal model and its Heating, Ventilating, and Air Conditioning (HVAC) system and provide very accurate simulation results useful for performance analysis and optimization process. In contrast, these energy simulators do not include sub-models of advanced devices/strategies for control of HVAC system operation and suffer from poor control mechanism. In addition to lack of an advance controller, they inherently offer no mechanism for estimating the future state of their process models based on forecast weather dataset. Hence, no predictive controller can be designed and implemented within these simulators. This paper discusses the development of a Matlab-TRNSYS co-simulator in order to control/manage a TRNSYS program, which was previously developed and calibrated based on the characteristics of a real case study house, with an advanced predictive controller. This co-simulator investigates the effectiveness of different predictive strategy planning models, including Load Shifting (LSH), Smart Dual Fuel Switching System (SDFSS), and LSHSDFSS, as the integration of fuel switching and load shifting strategy planning models on 24 h ahead energy cost saving of the case study house HVAC system. Simulation results of different consecutive sample days indicate that SDFSS could bring significant energy cost saving. However, LSH and LSHSDFSS effectiveness is sensitive to the outdoor temperature. [ABSTRACT FROM AUTHOR]

Abstract: An archetypal simulation model of office building representing variability in UK office building stock by parameterising built form, construction elements, occupancy/usage and operational/control strategy has been developed thus enabling detailed energy performance simulation to be used for stock modelling and parametric studies. The paper discusses the building characteristics needed to be considered for energy performance simulation, their values, and how they can be used in parametric studies. These parameters include built forms, fabrics (including thermal mass and insulation positioning), glazing percentages and characteristics, daylight and solar control measures and activity and operational related parameters (heating and cooling set points, ventilation rate, occupancy density and metabolic rate, equipment and lighting gain). The default parameter values suggested for the archetypal simulation model reflect typical existing and currently proposed UK office building stock. An archetypal model, combined with parametric studies, can be used in assessing energy performance of building stock and evaluating adaptation/retrofitting strategies. [Copyright &y& Elsevier]

Abstract: This paper described the development of regression models which are able to predict office building annual heating, cooling and auxiliary energy requirements for different HVAC systems as a function of office building heating and cooling demands. In order to represent the office building stock, a large number of building parameters were explored such as built forms, fabrics, glazing levels and orientation. Selected parameters were combined into a large set of office building models (3840 in total). As different HVAC systems have different energy requirements when responding to same building demands, each of the 3840 models were further coupled with five HVAC systems: VAV, CAV, fan-coil system with dedicated air (FC), and two chilled ceiling systems with dedicated air, radiator heating and either embedded pipes (EMB) or exposed aluminium panels (ALU). In total 23,040 possible scenarios were created and simulated using EnergyPlus software. The annual heating and cooling demands and their HVAC system''s heating, cooling and auxiliary energy requirements were normalised per floor area and fitted to two groups of statistical models. Outputs from the regression analysis were evaluated by inspecting models best fit parameter values and goodness of fit. Based on the described analysis, the specific regression models were recommended. [Copyright &y& Elsevier]

Abstract: Integrating passive systems such as cross and stack ventilation, a solar chimney, a wind tower or an earth-air tunnel into a building envelope has the potential to significantly reduce the ventilation and air-conditioning demand on buildings. However the application of combination of these features in low rise contemporary buildings is limited. Among the major reasons, is the lack of simple building modelling techniques preventing effective integrated passive systems. Existing coupled multi-zone airflow and thermal modelling software such as COMIS-TRNSYS, CONTAM-TRNSYS or TRNFLOW do not include the passive airflow components which require simultaneous prediction of temperature and airflow rate in the components such as solar chimneys and wind-induced earth-air tunnels. This paper develops an integrated model incorporating these passive airflow components into a coupled multi-zone ventilation and building thermal model. The model is validated against COMIS-TRNSYS software for a lightweight building with large openings. The prediction of each passive airflow component is validated with available published analytical and experimental findings. The solar chimney model is able to predict reverse flow and accurately predicts the air temperature in the chimney. The earth air tunnel (EAT) considers the transient heating up effect of the soil during operation and predicts the outlet air temperature. [Copyright &y& Elsevier]

Decarbonisation of the building sector is driving the increased use of heat pumps. As increased electrification of the heating sector leads to stress on the electricity grid, the need for district level coordination of these heat pumps emerges. This paper proposes a novel hierarchical coordination methodology, in which a price coordinator reduces the total instantaneous power demand of a building network below a power supply limit using a price signal. Each building has a model predictive controller (MPC) which maximises thermal comfort and minimises electricity costs. An additional term in the MPC objective function penalises the heat pump power demand quadratically, which when multiplied by a pseudo electricity price allows the price coordinator to reduce the peak power demand of the building network. A 2 building network is studied to analyse the price coordinator algorithm's behaviour and demonstrate how this approach yields a trade off between comfort, energy consumption and peak demand reduction. A 100 building network case study is then presented as a proof of concept, with the price coordinator approach yielding results similar to that of a centralised controller (less than 0.7% increase in energy consumption per building per year) and a roughly fourfold decrease in computation time. [ABSTRACT FROM AUTHOR]