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CURATOR
A pinboard by
Zakia Afroz

PhD student, Murdoch University

PINBOARD SUMMARY

Improving control over HVAC systems can provide a healthy and comfortable indoor environment.

Imagine that people are living or staying in a place that is not comfortable and good for their health whereas 80% of their total time per day is spent in a building. Because of this uncomfortable indoor environment people may not be as productive as they will be in a healthy indoor environment. Besides, working in a polluted environment can cause serious health problems such as respiratory diseases in the long run. On the other hand, research shows that implementing efficient energy management strategies in building HVAC system can lead to 5-15% energy savings in existing buildings. That is why, it is very important to control the building indoor environmental condition efficiently by building heating, ventilation and air-conditioning (HVAC) system to provide not only a healthy indoor environment but also to contribute to energy savings from the buildings. However, like speed is not the only performance criteria of a car, temperature is not the only parameter that should be controlled by HVAC system. Temperatures along with humidity, CO2 concentration, and volatile organic compound concentration inside the building are some of the performance parameters that should be controlled efficiently by HVAC system. The aim of my research will be balancing between building HVAC system performance parameters and energy consumption from this sector.

24 ITEMS PINNED

Occupancy behavior based Model Predictive Control for Building Indoor Climate-A Critical Review

Abstract: This paper reviews occupancy based Model Predictive Control (MPC) for building indoor climate control. Occupancy behavior in buildings is stochastic and complex in nature. With better understanding of occupancy presence in rooms and spaces, advanced controls, such as MPC, can be designed to achieve a more energy efficient operation, compared to more traditional control methods, while comfort is maintained. This paper starts with an overview of traditional controls implemented in buildings, and importance of occupancy based controls. Various control-oriented building modeling methods including physics-based and data-driven models are reviewed. Later on, a comprehensive review of MPC in terms of control theory, objective functions, constrains, optimization methods, system characteristics and various types of MPC is presented conducted. In principle, MPC finds an optimal sequence of control commands to optimize an objective function, considering system model, disturbances, predictions and actuation constraints. Lastly, occupancy based controls including commonly used rule-based and latest model-based controls are reviewed. In addition, a few experimental studies are presented and discussed. The paper presents a holistic overview of occupancy-based MPC for building heating, ventilation, and air conditioning (HVAC) systems, and discusses current status and future challenges. The purpose of this paper is to provide a guideline forresearchers who would like to conduct similar studies to have a better understanding of established research methods.

Pub.: 09 Aug '16, Pinned: 29 Aug '17

From occupancy to occupant behavior: An analytical survey of data acquisition technologies, modeling methodologies and simulation coupling mechanisms for building energy efficiency

Abstract: Publication date: February 2017 Source:Renewable and Sustainable Energy Reviews, Volume 68, Part 1 Author(s): Mengda Jia, Ravi S. Srinivasan, Adeeba A. Raheem Energy consumption and indoor environment of buildings are proved to be largely influenced by the presence and behaviors of occupants. The uncertainty caused by occupant behaviors accounts for a significant discrepancy between the predicted and actual energy usage. In a real world, building system operations and control will be directly affected by occupant behavior, which may lead to over thirty percent waste against building's designed performance. Therefore, the capability to seamlessly integrate occupant behavior in energy simulation tools and building management systems in the future is clearly important to optimize building energy use while maintaining the same level of services. However, research has not reached the phase that occupant behaviors could be effectively modeled. Thus, the traditional schedule based approach is not adequate to satisfy the needs of building efficiency. In this paper, a thorough survey of occupant behavior modeling and simulation state-of-the-art technologies and methodologies for building energy efficiency is conducted. The paper first identifies and discusses the significance and application scale of building occupant behavior model. Based on the information collected, some recent data acquisition technologies for behavior-related research and occupant behavior modeling approaches are summarized. The advantages and limitations of these modeling methods are compared and analyzed, as well as appropriate recommendations are made for the future research. The paper finally outlines the findings and potential development areas in the field of occupant behavior modeling for energy efficient buildings.

Pub.: 30 Oct '16, Pinned: 29 Aug '17

Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage

Abstract: Publication date: 1 February 2017 Source:Applied Energy, Volume 187 Author(s): Massimo Fiorentini, Josh Wall, Zhenjun Ma, Julio H. Braslavsky, Paul Cooper This paper describes the development, implementation and experimental investigation of a Hybrid Model Predictive Control (HMPC) strategy to control solar-assisted heating, ventilation and air-conditioning (HVAC) systems with on-site thermal energy generation and storage. A comprehensive approach to the thermal energy management of a residential building is presented to optimise the scheduling of the available thermal energy resources to meet a comfort objective. The system has a hybrid nature with both continuous variables and discrete, logic-driven operating modes. The proposed control strategy is organized in two hierarchical levels. At the high-level, an HMPC controller with a 24-h prediction horizon and a 1-h control step is used to select the operating mode of the HVAC system. At the low-level, each operating mode is optimised using a 1-h rolling prediction horizon with a 5-min control step. The proposed control strategy has been practically implemented on the Building Management and Control System (BMCS) of a Net Zero-Energy Solar Decathlon house. This house features a sophisticated HVAC system comprising of an air-based photovoltaic thermal (PVT) collector and a phase change material (PCM) thermal storage integrated with the air-handling unit (AHU) of a ducted reverse-cycle heat pump system. The simulation and experimental results demonstrated the high performance achievable using an HMPC approach to optimising complex multimode HVAC systems in residential buildings, illustrating efficient selection of the appropriate operating modes to optimally manage thermal energy of the house.

Pub.: 01 Dec '16, Pinned: 29 Aug '17

Predicting Energy Performance of a Net-Zero Energy Building: A Statistical Approach.

Abstract: Performance-based building requirements have become more prevalent because it gives freedom in building design while still maintaining or exceeding the energy performance required by prescriptive-based requirements. In order to determine if building designs reach target energy efficiency improvements, it is necessary to estimate the energy performance of a building using predictive models and different weather conditions. Physics-based whole building energy simulation modeling is the most common approach. However, these physics-based models include underlying assumptions and require significant amounts of information in order to specify the input parameter values. An alternative approach to test the performance of a building is to develop a statistically derived predictive regression model using post-occupancy data that can accurately predict energy consumption and production based on a few common weather-based factors, thus requiring less information than simulation models. A regression model based on measured data should be able to predict energy performance of a building for a given day as long as the weather conditions are similar to those during the data collection time frame. This article uses data from the National Institute of Standards and Technology (NIST) Net-Zero Energy Residential Test Facility (NZERTF) to develop and validate a regression model to predict the energy performance of the NZERTF using two weather variables aggregated to the daily level, applies the model to estimate the energy performance of hypothetical NZERTFs located in different cities in the Mixed-Humid climate zone, and compares these estimates to the results from already existing EnergyPlus whole building energy simulations. This regression model exhibits agreement with EnergyPlus predictive trends in energy production and net consumption, but differs greatly in energy consumption. The model can be used as a framework for alternative and more complex models based on the experimental data collected from the NZERTF.

Pub.: 14 Dec '16, Pinned: 29 Aug '17

Evaluation of energy conservation potential and complete cost-benefit analysis of the slab-integrated radiant cooling system: A Malaysian case study

Abstract: The energy-efficiency aspects and cost-saving features of various radiant cooling systems have been reported in many previous studies. However, the available literature on the analysis of initial investment and maintenance costs is scanty, especially for the slab-integrated one. A field survey was carried out in a green building in Malaysia that was installed with a combined radiant-convective system. The performance of this multi-floor radiant slab cooling system was studied and the energy conservation potential was evaluated by comparing the data obtained from the building energy management system (BEMS) with the estimated energy consumption of a conventional convective air system. Also, price quotations and service maintenance contracts were obtained for a complete cost-benefit evaluation of both systems. It was found that the radiant slab cooling system was able to reduce 34% of the energy consumption compared to the conventional variable air volume system while providing the same level of air conditioning. Despite its higher initial cost, the payback period for the investment cost is about 2.5 years. These findings are particularly useful for design engineers in the tropics to consider an alternative cooling technology for large office buildings which is more energy efficient and cost effective.

Pub.: 09 Dec '16, Pinned: 29 Aug '17

Inverse modeling of the urban energy system using hourly electricity demand and weather measurements, Part 1: Black-box model

Abstract: The difficulty of accurately assessing the ex ante impact of planned energy efficiency measures is a major barrier to the large-scale deployment of demand-side management (DSM) programs. The process of energy consumption in the urban built environment is dynamic in nature and comprised of the coupled interaction of multiple sub-systems. Furthermore, it usually displays significant correlation with weather and other perturbations. We propose an approach based on a novel linear/nonlinear black-box regression-based model of the load driven by exogenous variables. Model estimation is performed using actual hourly load and weather data. The data is used to generate a “baseline” or “business-as-usual” energy consumption model. Modifying some physically significant model parameters, and comparing the model prediction to the baseline reveals the energy savings that can result from planned future citywide energy efficiency interventions. The baseline model can also be used to test, a priori, candidate intervention scenarios by varying some of the physically significant parameters. We test this approach in the city of Abu Dhabi, UAE. For this location, we have reliable recording of both load and weather variables at hourly resolution. The proposed procedure, often referred to as inverse load modeling, presents several novelties that result in exceptional accuracy. The final model’s RMSE, does not exceed 1.6% of peak load while the MAPE is less than 2%. Finally, the descriptive nature of the model enables us to quantify the citywide impact of a DSM program comprised of several basic energy efficiency interventions.

Pub.: 22 Jan '17, Pinned: 29 Aug '17

A two-stage Energy Management System for smart buildings reducing the impact of demand uncertainty

Abstract: This paper proposes a novel two-stage Energy Management System (EMS) that is suitable for small-scale grid-connected electrical systems, such as smart homes and buildings, encompassing renewable generators and electrical storage. In such systems, forecast errors of renewable generation and energy demand profiles result in a significant uncertainty on the power exchanged between the end users and the utility grid. The proposed EMS reduces such demand uncertainty and the electricity bill for end users, at the same time. The main novelty of the proposed technique is that it does not require any change in pricing plans or user's habits, differently from classical Demand Side Management schemes. Moreover, thanks to the increased predictability of the exchanged power, utility providers are facilitated in managing the wholesale risk, for example by designing appropriate pricing schemes. The proposed EMS is based on an optimization algorithm. It starts from profiles of renewable generation and load demand, which are obtained by a forecasting method based on suitably chosen and trained Artificial Neural Networks. Furthermore, it has been designed to be suitable for an embedded implementation on low-performance processing platforms. The proposed EMS has been validated using datasets coming from monitoring campaigns. The considered case study is a smart home with an annual energy consumption of about 4500 kWh. It encompasses a grid-connected electrical distribution power plant with a 3 kW photovoltaic generator and a 4.6 kWh battery electrical storage system. The results obtained for a sample month demonstrate the effectiveness of the approach. As a matter of fact, the demand uncertainty is only 4.75% against a cumulative forecast error of 10.35% expressed as normalized root mean square error. At the same time, the end user's cash flow is 2.43% higher than the income obtained without an EMS.

Pub.: 04 Jan '17, Pinned: 29 Aug '17

Interaction Effects of Building Technology and Resident Behavior on Energy Consumption in Residential Buildings

Abstract: Buildings account for a significant portion of energy consumption and carbon emissions around the world and increasingly scholars and practitioners are re-thinking strategies that mitigate use. This paper reports an empirical study aimed at identifying the relationship between building technology and resident behavior and the joint effects on energy consumption in residential buildings. Unlike previous work that isolated effects of technology or behavior on energy consumption, this study investigates their interactions. The researchers collected technical and behavioral data from more than 300 residential units and performed data analysis using energy simulation and multivariate regression techniques. Results identify the interaction effects between building technology and resident behavior and provide quantifiable evidence supporting the hypothesis that “building technology and resident behaviors interact with each other and ultimately affect home energy consumption.” Findings indicate four important resident behaviors that directly correlate to energy consumption and two that indirectly correlate to energy consumption. The research also indicates that only 42% of technological advances directly contribute to home energy efficiency, suggesting that the achievable impact on energy savings depends on both technical advances and behavioral plasticity.

Pub.: 02 Nov '16, Pinned: 31 Jul '17

An evaluation index for the control effect of the local ventilation systems on indoor air quality in industrial buildings

Abstract: Abstract To evaluate the control effect on indoor air quality (IAQ) of the local ventilation systems in industrial buildings with centralized contaminant sources, a new index, namely, normalized concentration in the target zone (NC-TZ), was proposed in this paper. According to theoretical analysis, NC-TZ is non-dimensional and ranges from 0 to 1. When NC-TZ tends toward 0, the control effect of the local ventilation system on IAQ is more satisfactory. When NC-TZ tends toward 1, the control effect on IAQ is less satisfactory. The numerical simulation on a push–pull ventilation system with varying exhaust flow rates and varying distances between push and pull hoods was performed. The results demonstrate that for the same capture efficiency, changing the local ventilation system characteristics can change the control effect on the local environment. The results for obstacles at different positions also indicate that NC-TZ can clearly reflect the control effect on IAQ of the local ventilation systems in industrial buildings.AbstractTo evaluate the control effect on indoor air quality (IAQ) of the local ventilation systems in industrial buildings with centralized contaminant sources, a new index, namely, normalized concentration in the target zone (NC-TZ), was proposed in this paper. According to theoretical analysis, NC-TZ is non-dimensional and ranges from 0 to 1. When NC-TZ tends toward 0, the control effect of the local ventilation system on IAQ is more satisfactory. When NC-TZ tends toward 1, the control effect on IAQ is less satisfactory. The numerical simulation on a push–pull ventilation system with varying exhaust flow rates and varying distances between push and pull hoods was performed. The results demonstrate that for the same capture efficiency, changing the local ventilation system characteristics can change the control effect on the local environment. The results for obstacles at different positions also indicate that NC-TZ can clearly reflect the control effect on IAQ of the local ventilation systems in industrial buildings.

Pub.: 01 Dec '16, Pinned: 31 Jul '17

Thermal adaptive models in the residential buildings in different climate zones of Eastern China

Abstract: Adaptive comfort standards have become the main stream comfort research and are now considered an optional choice of natural ventilated buildings in the international thermal comfort standards. However, the international adaptive models were not suitable to evaluate the thermal adaptation level of all the climates. To explore thermal adaptive ability and develop thermal comfort models in different climate zones, field studies on thermal comfort in 120 residential buildings in summer and winter have been conducted in 12 cities, representative of four climate zones in eastern China. Those data were gathered using instantaneous subjective questionnaire surveys and objective on–site measurements. The results showed that the predicted neutral temperatures based on MTS in winter in four climate zones were all lower than the predicted neutral temperatures based on PMV, and vice versa in summer. The clothing was mainly affected by the indoor temperature in the severe cold climate; however, it was affected by the outdoor temperature in the warmer climates. Clothing adjustment was more obvious in the warmer climate than in the colder climate. The warmer the climate, the smaller the yearly temperature difference, and the higher a sensitivity of the neutral temperature to outdoor temperature. The adaptive models in the hot summer and cold winter zone (HSCW) and hot summer and warmer winter zone (HSWW) can be used to predict the comfort temperatures of the natural ventilated buildings in the above two climate zones. Different climate zones should develop their own thermal adaptive models. These findings provide support to the climate adaptation theory and can serve as reference for the design of natural ventilated buildings.

Pub.: 10 Feb '17, Pinned: 31 Jul '17