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In this paper, the problem of dispatching the operation of a distribution feeder comprising a set of heterogeneous resources is investigated. The main objective is to track a power trajectory, called the dispatch plan , which is computed the day before the beginning of operation. In this paper, we propose a method to optimally compute the dispatch plan so as to optimize the operation of the feeder while making sure to allocate enough local reserves to absorb deviations of the realizations. Indeed, during real-time operation, due to the stochasticity of part of the resources in the feeder portfolio, tracking errors need to be absorbed in order to track the committed dispatch plan. This is achieved by modulating the power consumption of a utility-scale battery energy storage system and of the heating, ventilation and air conditioning system of a commercial controllable building. To this end, a hierarchical controller is designed to coordinate these two controllable entities while requiring a minimal communication infrastructure. Due to the inherent different response times of these systems, the power injection of the electrical battery is controlled at a sub-minute time-scale so as to absorb high-frequency tracking errors and, therefore, deliver the dispatch service. At a slower time-scale, the controllable building is controlled to maintain the state of charge for the electrical battery at a scheduled level by means of a model predictive controller. The model predictive controller is designed in order to account for both comfort and operational constraints of the controllable building, as well as power limits for the electrical storage. The effectiveness of the proposed control framework is demonstrated by means of both an extensive simulation analysis, as well as a set of 12 full day experimental results on the 20 kV distribution feeder in the campus of the Swiss institute of technology in Lausanne, that is comprised of: 1) A set of uncontrollable resources represented by five office buildings (350 kWp) and a roof-top photovoltaic installation (90 kWp), 2) a set of controllable resources, namely, a grid-connected electrical storage (720 kVA–500 kWh), and a fully-occupied multi-zone office building (45 kWp). [ABSTRACT FROM AUTHOR]

Abstract Understanding occupants’ thermal sensation and comfort is essential to defining the operational settings for Heating, Ventilation and Air Conditioning (HVAC) systems in buildings. Due to the continuous impact of human and environmental factors, occupants’ thermal sensation and comfort level can change over time. Thus, to dynamically control the environment, thermal comfort should be monitored in real time. This paper presents a novel non-intrusive infrared thermography framework to estimate an occupant's thermal comfort level by measuring skin temperature collected from different facial regions using low-cost thermal cameras. Unlike existing methods that rely on placing sensors directly on humans for skin temperature measurement, the proposed framework is able to detect the presence of occupants, extract facial regions, measure skin temperature features, and interpret thermal comfort conditions with minimal interruption of the building occupants. The method is validated by collecting thermal comfort data from a total of twelve subjects under cooling, heating and steady-state experiments. The results demonstrate that ears, nose and cheeks are most indicative of thermal comfort and the proposed framework can be used to assess occupants’ thermal comfort with an average accuracy of 85%. [ABSTRACT FROM AUTHOR]

Real-time optimization (RTO) has been developed to improve the cost and/or energy efficiency of complex heating, ventilation and air-conditioning (HVAC) systems. In current literature, almost all of the developed real-time optimization methods belong to the type of time-driven optimization, in which the action of optimization is triggered by “time”. As optimization should be done when the system operating conditions experience a change that is large enough to cause current operational setting not optimal any more, optimization strategies should recognize ‘significant’ changes and perform optimization when necessary. Since the time-driven optimization is a periodic mechanism in nature while those ‘significant’ changes may not be periodic, the time-driven optimization cannot capture ‘significant’ changes and do the optimization promptly. Therefore, this paper proposes an event-driven optimization (EDO) for complex HVAC systems, the key idea of which is to use “event” rather than “time” to trigger the action of optimization. A systematic event-driven optimization method is illustrated, where its main tasks, including event definition and event identification, will be discussed. The proposed method will be compared with a conventional time-driven method using case studies, through which the main advantages of the proposed method will be identified. [ABSTRACT FROM AUTHOR]

• Moisture needed to be removed in three building types is calculated. • Salt supported porous composites satisfy most climates and building types. • Zeolites are suitable for return air state, ARI summer and ARI humid climates. • Polyelectrolytes can cope with humid climates and buildings with high wet load. Energy conservation and emission reduction have become a global consensus. As a novel promising energy-saving dehumidification technology, desiccant coated heat exchanger (DCHE)-based air conditioning systems have attracted immense attention recently. This paper provides a quick and reasonable selection of desiccant materials for DCHE systems using water as the cooling medium in different buildings and climates. By the theoretical calculation of moisture needed to be removed in three types of buildings (offices, residential buildings and restaurants) and the analysis of the dehumidification capacity of desiccants in DCHEs reported in the literature, optimal desiccants for different building types and ambient air conditions under given parameters are recommended. Salt-supported porous composite desiccants possess flexible dehumidification performance and can be utilized in most climates and building types, except for extremely humid indoor environments with high requirement for fresh air. Zeolites are especially suitable for return air states, American Air-Conditioning and Refrigeration Institute (ARI) summer and ARI humid climates in buildings with moderate indoor moisture gain, such as offices or residential buildings. Polyelectrolytes can be applied in humid climates and buildings with high indoor wet loads, such as restaurants. This paper offers a promising route to the applications of desiccant materials in novel energy-saving air conditioning technologies. [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]

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]

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]

Heating, ventilating, and air-conditioning (HVAC) systems consume almost half of the total energy consumption in buildings. In order to reduce the energy consumption of HVAC systems in buildings, different passive heating and cooling strategies and thermal energy storage systems have been adopted. Water wall is an excellent short-term thermal energy storage system which can be used to maintain thermal comfort in buildings while reducing the heating and/or cooling load of the buildings. A significant amount of research work on water wall has been carried out since the 1980s. This paper presents a comprehensive review of the previous studies of water wall systems, focussing mainly on the performance of various configurations of water wall systems and the different research approaches adopted in the literature. The advantages and disadvantages of the different research approaches have been highlighted. It is revealed that the existing water wall research has been based mainly on an energy balance model and experimental modelling, but CFD (computational fluid dynamics) modelling has attracted little attention. Moreover, this review has identified several gaps in the water wall research for further attention. [ABSTRACT FROM AUTHOR]

Energy signatures for air conditioning systems can have characteristics which are not seen with heating systems. This paper explains and illustrates some of the characteristics that are specific to air conditioning systems and describes how energy signatures that take account of them can be applied to produce benchmarks, control charts and diagnostic information. It focusses on the use of energy signatures derived from measured daily system energy consumption. Daily energy signatures can generate more robust energy consumption benchmarks and provide additional insight into unusual energy demand patterns compared to monthly or weekly signatures, albeit requiring slightly more data. In particular, they distinguish between weekday and weekend consumptions. They can be used to generate benchmarks based on standardised annual consumption or standardised annual load factor. In addition they can be used to generate control charts to identify days of unusual consumption for individual systems. More sets of daily energy consumption data are needed to evaluate their diagnostic power. [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]

In practice, monthly temperature records play a major role calculating the heating and cooling degree-days of the air conditioner management in buildings. Classically, heating and cooling degree calculations are based on the comparison of outdoor temperature measurements with a standard level called the base temperature. Truncating the temperature series at a constant base temperature causes heating (deficits) and cooling (surpluses) degrees. Since outdoor temperature records are of random character, future predictions with probabilistic and statistical methods are convenient means for calculations. All previous methodologies in the literature are based on crisp logic with simple mathematical calculations. This paper proposes a new methodological approach by converting the actual temperature record probability distribution function (PDF) into a probabilistic standardized degree indicator (SDI) in normal (Gaussian) PDF form with zero mean and unit standard deviation. Furthermore, as a new evaluation methodology fuzzy logic inference calculations are provided. SDI is divided into seven classes, three for cooling, heating, and a single class for uncooled-unheated states. Heating and cooling degree changes according to SDI values are presented with crisp and fuzzy sets. The application of the methodology is made for Kadıköy urban area on the Asian side of Istanbul, Turkey. In the case of fuzzy set SDI air conditioned operation, it has been determined that there is cooling energy saving of approximately 25% compared to the crisp set. The same is true for the heating degree-day side. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) for building applications has been considered by many HVAC experts as an efficient tool to promote building energy efficiency and indoor thermal comfort. This paper, therefore, proposes a strategy of using WSN to monitor the temperature distribution in a large-scale indoor space, which aims to (i) improve the quality of the measurements transmitted by wireless signals, (ii) identify the temperature distribution pattern of the large-scale space, and (iii) optimize the allocation of supply air rated flow rate to multiple supply air terminals that serve the space according to the identified temperature distribution pattern. The developed strategy is illustrated by applying it to a real WSN test bed, installed in a typical large-scale space in Hong Kong. Results are presented and discussed, which shows that the phenomenon of temperature uneven distribution was commonly existent in a large-scale space and the developed strategy was able to efficiently improve the quality of measurements by WSN and improve the temperature homogeneity in the large-scale space. [ABSTRACT FROM AUTHOR]