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In buildings, occupant behaviour is recognised as a major contributing factor to energy demand and in particular to heating consumption. To achieve thermal comfort within the heating season, people report to use heat in very different ways; for example behaviours include switching on the heating system, putting on warm clothes, drawing curtains, changing rooms, making a hot drink and using a hot water bottle. While research has focused on subjective accounts using interviews, diaries and questionnaires, little is known about the frequency and probability of these behaviours. Using a mixed-method approach, this paper reports on the results of a field study in dwellings using wearable and environmental sensors. The analysis investigates the probability of these behavioural responses as a function of seven independent variables; (1) external and (2) internal monitored temperature, (3) probability of heating being on or off, (4) time of the week, (5) time of the day, (6) the three categories of the predictive thermal comfort model, and (7) the three categories of the adaptive thermal comfort model. Results show that participants were more likely to increase their clothing and activity level as internal temperature decreased, although there was no significant change in activity level throughout the course of a day. Methodologically, this paper demonstrates the effectiveness of different statistical tools in analysing occupants’ behaviours. Substantively, this paper emphasises the need for future research to gather objective data on what people do. [ABSTRACT FROM AUTHOR]

Green roofs have been proposed for energy saving purposes in many countries with different climatic conditions. However, their cooling and heating potential strongly depends on the climate and building characteristics. In particular, the increase of the thermal capacity of green roofs compared to traditional roofs, if not controlled with insulation, may lead to higher cooling and heating loads. This paper discusses the energy saving potential of green roofs adopting a variable insulation strategy. A system consisting of a plenum located between a green roof and the room underneath and a sensor-operated fan that couples (or decouples) the green roof mass with the indoor environment was developed. The fan is activated and stopped using temperature based rules; the plenum is ventilated only when the fan works, creating a variable insulation system. Four cells with an insulated traditional roof, a non-insulated green roof, an insulated green roof, and a green roof with the variable insulation system have been tested in a hot and dry climate with mild winters over several years. This paper compares and discusses different plenum control algorithms. Results are particularly promising because the variable insulating system proved to adjust the thermal capacity of the roof effectively. In summer, the non-insulated green roof and the green roof with variable insulation system achieved the lowest indoor temperature; in winter, the insulated traditional roof and the variable insulation green roof system achieved the highest indoor temperatures. Measurements are hence compared with simulations. Finally, the energy saving potential of the new green roof system is evaluated. [ABSTRACT FROM AUTHOR]

Thermal energy storage (TES) systems with phase change materials (PCMs) as a known energy storage technology have a high potential for increasing the energy efficiency of buildings. In fact, the use of PCMs with various approaches, either active or passive, in the building helps to maintain the temperature in the thermal comfort range for occupants through decreasing the temperature swings, and lower energy consumption by the load reduction/shifting. Due to the significance of this issue, many research works have been carried out on the application of PCMs in buildings. In this study, the experimental works in the fields of PCMs in buildings are taken into account. The papers are classified based on heating, cooling, and air-conditioning. Besides these, the applications of nano-PCMs in buildings are reviewed. According to what has been carried out up to now, the gaps are identified and analyzed for preparing useful suggestions for future works. The recommendations and suggestions are presented at the end of the chapters as well as the conclusion, and the literature is summarized in some tables. [ABSTRACT FROM AUTHOR]

Abstract: The increase of peak and energy demand during the cooling season is becoming a crucial issue, as well as the intensification of the urban heat island effect. This trend is observed at several latitudes, including areas where overheating was unknown at building and urban levels. This phenomenon involves different issues: reduction of greenhouse gases, quality and comfort in outdoor and indoor environment, security of energy supply, public health. The building sector is directly involved in this change and adequate solutions can provide great benefit at energy and environmental levels. Roofs in particular are envelope components for which advanced solutions can provide significant energy savings in cooled buildings or improve indoor thermal conditions in not cooled buildings. Cool materials keep the roof cool under the sun by reflecting the incident solar radiation away from the building and radiating the heat away at night. Roofs covered with vegetation take benefits of the additional thermal insulation provided by the soil and of the evapo-transpiration to keep the roof cool under the sun. These two technologies are different in: structural requirements, initial and lifetime maintenance costs, impact on the overall energy performance of buildings. This paper presents a numerical comparative analysis between these solutions, taking into account the several parameters that affect the final energy performances. By means of dynamic simulations, the paper depicts how cool and green roofs can improve the energy performance of residential buildings in different localities at Mediterranean latitudes. [Copyright &y& Elsevier]

There is a growing trend for the use of thermo-active building systems (TABS) for the heating and cooling of buildings, because these systems are known to be very economical and efficient. However, their control is complicated due to the large thermal inertia, and their parameterization is time-consuming. With conventional TABS-control strategies, the required thermal comfort in buildings can often not be maintained, particularly if the internal heat sources are suddenly changed. This paper shows measurement results and evaluations of the operation of a novel adaptive and predictive calculation method, based on a multiple linear regression (AMLR) for the control of TABS. The measurement results are compared with the standard TABS strategy. The results show that the electrical pump energy could be reduced by more than 86%. Including the weather adjustment, it could be demonstrated that thermal energy savings of over 41% could be reached. In addition, the thermal comfort could be improved due to the possibility to specify mean room set-point temperatures. With the AMLR, comfort category I of the comfort norms ISO 7730 and DIN EN 15251 are observed in about 95% of occasions. With the standard TABS strategy, only about 24% are within category I. [ABSTRACT FROM AUTHOR]

To date, religious facilities have received little attention in the published literature regarding energy use and efficiency. With unique operating and building characteristics, these buildings have distinct opportunities for analysis in regards to building energy use and occupant comfort. With overall lower average energy intensity, religious facilities rarely receive energy assessments or are operated by experienced personnel. Therefore, identifying common issues is critical for training church owners in energy efficiency. This paper presents an in-depth analysis of heating, ventilation and air conditioning (HVAC) systems and occupant comfort in two religious facilities. The analysis reveals that the largest opportunities for reduction in energy use come from proper maintenance and operation of HVAC equipment and schedules. Temperature setbacks are shown to be an important operational setting to reduce energy usage. An accompanying analysis on thermal comfort reveals that temperature setbacks must be coupled with sufficient preconditioning of the space to ensure occupant comfort during intermittent building occupancy. An analysis of CO 2 levels in the building during occupancy reveals the need for sufficient outside air to meet ventilation standards. [ABSTRACT FROM AUTHOR]

Current indoor thermal environment is usually automatically controlled based on a temperature set-point given by room occupants or building managers. However, investigation shows that many temperature set-points are far from the comfortable temperature range recommended by design handbooks. Such unreasonable temperature set-points will result in not only uncomfortable indoor thermal environment, but also waste of cooling or heating energy. The authors have proposed a methodology of satisfaction based control to take place of the traditional set-point based control. This paper describes the experimental comparison results between satisfaction based indoor thermal environment control and set-point based control. Two test-beds were set up and a series of experiments was conducted to compare control performances, user acceptances, user work performances and system energy consumptions of the set-point based and satisfaction based control. The comparison results show that the satisfaction based control can achieve more stable thermal environment. Both of the two control methods can get relatively high scores regarding user acceptance and work performance. There are no statistically significant differences for user acceptance and work performance between two control methods. However, satisfaction based control consumed 15.3% and 11.9% less energy at two test-beds respectively than set-point based control due to more reasonable temperature settings given by the satisfaction based control. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) has been studied in the building science realm for about three decades. However, the following two aspects of the building control have not been studied thoroughly in MPC research. One is the impact of the mixed-mode cooling system on the active heating ventilation and air conditioning (HVAC) energy consumption, and the other is the differences of individual thermal comfort preference and its impact on energy. This paper proposes an occupant-oriented mixed-mode EnergyPlus predictive control system to optimize HVAC energy consumption while meeting the individual thermal comfort preference. A web-based dashboard is implemented in the test-bed building for three months to collect individual thermal comfort preference data. The data analysis results suggest that occupants have various tolerances and preferences about thermal comfort. The simulation results show that, during one week of a typical swing season, the mixed-mode system further reduces the active HVAC energy consumption, and the diversified occupant thermal comfort preference has significant impact on HVAC energy consumption. [ABSTRACT FROM AUTHOR]

In buildings, HVAC systems consume a high amount of energy to provide thermal comfort for occupants. A methodology is presented in this paper to control thermostat operation of the buildings considering the effects of indoor and outdoor boundary conditions and phase change material (PCM) characteristics. EnergyPlus v8.1 building energy simulation software was used to analyze the energy performance of the PCM incorporated building models and to implement Fanger model to control HVAC thermostat operation according to BS EN 15251:2007 thermal comfort categories. Three types of building HVAC schedule, PCM with different melting points and layer thicknesses were studied for Madrid climate zone. Moreover, the impact of occupants clothing on the energy consumption was investigated. Furthermore, payback analysis was conducted to find out the economic benefits of PCM integration into the building envelopes. Application of PCM improved the cooling and heating energy performances except for the office model in winter (heating period). Additionally, higher energy savings and lower payback periods were observed when PCM with higher melting point was applied to the buildings. Eventually, energy savings in PCM incorporated models were found to improve further when occupants changed their clothing behavior in winter. [ABSTRACT FROM AUTHOR]

This paper presents a cooperative fuzzy model predictive control (CFMPC) for heating and cooling of buildings. Because of different supply zones, the large time constants and the non-linear building dynamics with respect to the different seasons, a CFMPC concept is proposed. The overall non-linear building is split into different zones, which are consisting of input-coupled Takagi-Sugeno (TS)-fuzzy models. Each such TS-fuzzy model is constituted by a local linear model network (LLMN). The LLMN consists of local linear models (LLM), which are representing the different seasons: winter, transition season (fall and spring), and summer. The control of each LLM is realized by model predictive control (MPC). For each building zone the associated MPCs are output-blended by the fuzzy membership functions, which leads to fuzzy model predictive control (FMPC). In addition to the FMPCs a global MPC is controlling the thermally activated building systems, which affect all other zones. To coordinate the different controllers a cooperative iteration-loop is assumed, which leads to cooperative fuzzy model predictive control (CFMPC). The concept is developed for a specific demonstration building and can be easily adapted for other complex buildings. A simulation example demonstrates that the proposed CFMPC achieves a performance increase with less energy consumption, as compared to FMPC controllers and historical measured data of the demonstration building. [ABSTRACT FROM AUTHOR]

Passive Houses are buildings which provide comfortable indoor conditions at an extremely low heating and cooling load. The peak daily average heating and cooling loads are typically below 10 W/m 2 and annual useful energy demands are below 15 kW h/(m 2 a). The Passive House standard was originally developed in Germany. In this paper we show by hygro-thermal dynamic simulation that it is possible to realize residential Passive Houses in all of the world's relevant climate zones, represented here by Yekaterinburg, Tokyo, Shanghai, Las Vegas, Abu Dhabi, and Singapore. The window quality, insulation levels, and mechanical services all depend on the climate as well as on the building's shape and orientation. The resulting annual energy demand for space conditioning of the Passive Houses is 75 to 95% lower than that of a traditionally insulated building of the same geometry. In humid climates like Shanghai or Singapore, special attention must be paid to humidity aspects. In climates which are hot and humid all year long, the total useful energy demand for sensible and latent cooling may exceed 70 kW h/(m 2 a) even in a Passive House. Finally, it is shown that the architectural quality is not compromised by the Passive House requirements. [ABSTRACT FROM AUTHOR]

Several studies have shown that the actual energy performance of buildings can differ significantly from its designed value. An important part of this performance is constituted by the building fabric's thermal performance. A common method to evaluate the latter for an actual building is the co-heating test. The co-heating test comprises a quasi-stationary heating experiment followed by linear regression analysis of aggregated building performance data. This paper reviews related research work and cristallises the current state-of-the-art. The physical phenomena working behind the scenes of the generally assumed simplified heat balance are discussed. Statistical constraints generally prevent these from being uncovered fully during the analysis. Multiple linear regression is proposed as the most sensible method to analyse co-heating measurement data. A novel way to visualise such analysis, deduce building performance data graphically and compare different co-heating test results is presented. [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: 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]