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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]

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]

Urban building energy modeling (UBEM) is a valuable tool for analyzing the building stock. Many different model approaches have been proposed in recent years suggesting various ways of dealing with the challenges of UBEM; however, central for all modeling approaches is the need for informative input data about the building stock. The availability of data for urban-scale modeling is both country-specific, time-consuming to aggregate, and data access is often limited due to privacy constraints. In this paper, we present a hierarchical bottom-up model of the Danish residential building stock using public building data for predicting the annual heating energy consumption. For more than 10,000 randomly selected single-family dwellings, the annual energy consumption is modeled and validated for the city of Aarhus, Denmark. We found that approx. 50% of the energy use is explained using only four widely available building characteristics, which enables building-scale predictions with a mean absolute error of approx. 25%. In addition, for city-scale predictions, the regression-based model enables aggregated predictions with a mean bias error of less than ±2%. Even though building-scale predictions are only somewhat accurate, the performance remains comparable to state-of-the art high-fidelity models in the literature. [ABSTRACT FROM AUTHOR]

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]

The capability to dynamically plan, predict, and control indoor conditioning allows to adapt to individual preferences of inhabitants and enables demand side management. While former mainly improves thermal comfort of inhabitants so does latter unlock ecological and financial opportunities mostly for energy utilities. Commonly, dynamic indoor conditioning is based on piece-wise constant indoor temperature constraints. This paper's contribution is the presentation of additional constraints: in particular ones expressed relative to the nominal behavior of a hydronic heating system. This allows to simultaneously harness the relevant process variables in particular during the pre-loading and post-loading phases of a load reduction. The findings are based on data sets acquired on 10 inhabited, residential buildings in Stockholm over a whole year. One of the findings is that building models need to be adaptable if predictive control is applied in practice. This adaptability is assured by a novel concept, i.e. a so called model manager on which the control is relying for the selection of the most accurate model. Centralized optimal control of buildings connected to a district heating network is challenging in practice due to a high computational load. In order to reduce it, the herein presented method elaborates plans only every hour instead of at every control step for optimal control. Since these plans cannot be optimal due to the lack of regular update a hitherto unknown cascaded control logic has been developed that corrects planning errors and other disturbances. Capabilities are demonstrated and compared to conventional controllers in dynamic simulations of a multi-zoned building. The herein presented method is to our knowledge the first to provide all flexibility desired by energy utilities and inhabitants alike through harnessing the consequences of transitions. [ABSTRACT FROM AUTHOR]

• Nonlinear hybrid MPC for buildings energy systems based on Modelica. • Time-variant linearization of the nonlinear Modelica optimization problem. • Automated creation of the linearized optimization problem in every MPC iteration. • Integration of on/ off control, discrete operation states and operation modes. This paper presents a nonlinear hybrid Model Predictive Control (MPC) approach for building energy systems based on Modelica. The MPC approach takes into account two characteristics that are very common for building energy systems: nonlinearities (inherent in the building envelope and Heating, Ventilation and Air Conditioning (HVAC) systems) and discontinuities (in the form of on/ off operation, discrete operation states and operation modes). The hybrid MPC approach integrates both continuous and discrete optimization variables into the control concept and thus is capable of controlling building energy systems with binary or integer decision variables, switching dynamics or logic if-then-else constraints. By employing a time-variant linearization approach, nonlinear Modelica optimization problems are approximated with high accuracy and transformed into a linearized state-space representation. Based on the linearization output, a linearized optimization problem is generated automatically in every MPC iteration, which is extensible by various integer characteristics and is accessible for a wide range of mixed-integer solvers. A simulation study on a nonlinear Modelica building energy system demonstrates the control quality of the proposed toolchain revealing a small linearization error and successful integration of multiple integer characteristics. The benefits of the approach are manifested by comparing its performance with different reference control strategies. [ABSTRACT FROM AUTHOR]

Abstract: HVAC energy savings have been a key focus of recent building energy management efforts, but the savings typically reduce thermal comfort for building occupants. Established thermal comfort models use complex and person-specific parameters, such as clothing insulation and metabolic rate, to predict individual comfort levels, making the design of automated comfort modelling systems a highly challenging endeavour. In this paper, we investigate the use of humidex, which encapsulates both temperature and humidity, as an easily measurable and highly representative indoor thermal comfort predictor. We verify the feasibility of humidex as an indoor thermal comfort predictor by contrasting its performance to that of the best feature set (the feature set that best predicts the thermal comfort) constructed jointly by recursive sequential forward selection and support vector regression. The analysis using the global datasets, including data from seven climate zones across three continents, shows that humidex is a favourable and easily measurable indoor thermal comfort descriptor when humidity is significant. We take away the message from the analysis and design, develop, and deploy a system that couples a deployment of sensor nodes in an office environment, where each node collects the ambient temperature and humidity at each person's desk, and an automated survey mechanism to record people's thermal sensation votes. We use subjective response through the surveys as ground truth to validate the performance of the thermal comfort prediction using humidex. The results confirm our analysis of global datasets and show that humidex is a good predictor of indoor thermal comfort at high humidity. [Copyright &y& Elsevier]

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]

• A hybrid optimization algorithm in online optimization of MPC is proposed. • A distributed MPC for multi-zone buildings existing input coupling is studied. • Nash-optimization acts on enhancing the control performance in distributed MPC. • The redefined global performance index provides the possibility to save energy. To meet demands of smart heating development, the novel dynamic regulation methods of heating systems need to be pursued. This paper presents a model predictive control (MPC) approach for room temperature regulation in multi-zone buildings. Among them, a) For heating systems with nonlinearity and large time delay, a hybrid optimization algorithm consisting of the improved particle swarm optimization (IPSO) algorithm and Newton-Raphson (NR) method in online optimization of MPC for a single zone building is proposed. Compared with the quadratic programming method, it can identify successfully the optimal control vector. b) A distributed MPC approach is presented for multi-zone buildings existing hydraulic coupling. Firstly, Nash-optimization acts on searching the global equilibrium point in the distributed structure, then the hybrid optimization algorithm works in the online optimization of these local MPCs. Compared with the decentralized MPC, the control performance is improved through co-operating and communicating between subsystems via network to share information related to their future behavior. Besides, the global performance index in the distributed MPC is redefined as the weighted sum of energy consumption and thermal comfort, which provides the possibility to eliminate quickly the most unfavorable thermodynamic loop in heating systems and reduce energy consumption. [ABSTRACT FROM AUTHOR]

• Demand-Side-Management with air-to-water heat pump in a Nearly-Zero-Energy-Building. • Model Predictive Control with Artificial Neural Network for heat demand prediction. • Variation of storage capacity and heat pump power shows cost savings up to 24.3% • Market based concepts need further incentives to exploit the flexibility potential of heat pumps. The rollout of the supply dependent generators wind turbines and photovoltaics leads to a flexibility demand that can be adressed from the consumer side, via Demand-Side-Management, as well. In single family houses, the heat pump, in combination with thermal energy storage, can shift their energy comsumption according to price signals in order to reduce consumer costs. This paper analyses the impact of different heat storage sizes and heat pump powers on cost savings and shifting potential, focussing on the Luxembourgish context, when variable electricity prices based on the electricity market are applied. A model predictive controller determines the cost-optimal operating cycles of the heat pump. The building's heat demand is predicted with the help of a neural network. The results of the parametric study show significant differences in energy efficiency and cost savings. Furthermore limitations of taking advantage of variable electicity prices due to the price structure are disclosed. The cost savings however do not give a sufficient incentive for the consumer to invest in optimizing the heating system for Demand-Side-Management purposes. By consequence, the potential and the efficiency of Demand-Side-Management are limited and further incentives are necessary. [ABSTRACT FROM AUTHOR]

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]