ORCID Profile
0000-0002-4084-9709
Current Organisations
University of Colorado at Boulder
,
Technical University of Berlin
,
National Renewable Energy Laboratory
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Elsevier BV
Date: 10-2010
Publisher: Elsevier BV
Date: 02-2003
Publisher: American Society of Civil Engineers (ASCE)
Date: 12-2001
Publisher: Elsevier BV
Date: 02-2011
Publisher: Elsevier BV
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 10-03-2015
Publisher: Elsevier BV
Date: 04-2019
Publisher: Elsevier BV
Date: 04-2013
Publisher: ASME International
Date: 02-2005
DOI: 10.1115/1.1824110
Abstract: Cooling of commercial buildings contributes significantly to the peak demand placed on an electrical utility grid. Time-of-use electricity rates encourage shifting of electrical loads to off-peak periods at night and on weekends. Buildings can respond to these pricing signals by shifting cooling-related thermal loads either by precooling the building’s massive structure or by using active thermal energy storage systems such as ice storage. While these two thermal batteries have been engaged separately in the past, this paper investigates the merits of harnessing both storage media concurrently in the context of optimal control for a range of selected parameters. A parametric analysis was conducted utilizing an EnergyPlus-based simulation environment to assess the effects of building mass, electrical utility rates, season and location, economizer operation, central plant size, and thermal comfort. The findings reveal that the cooling-related on-peak electrical demand and utility cost of commercial buildings can be substantially reduced by harnessing both thermal storage inventories using optimal control for a wide range of conditions.
Publisher: Elsevier BV
Date: 11-2017
Publisher: ASME International
Date: 27-01-2003
DOI: 10.1115/1.1532005
Abstract: This paper investigates adaptive optimal control of a grid-independent photovoltaic system consisting of a collector, storage, and a load. The control algorithm is based on Q-Learning, a model-free reinforcement learning algorithm, which optimizes control performance through exploration. Q-Learning is used in a simulation study to find a policy which performs better than a conventional control strategy with respect to a cost function which places more weight on meeting a critical base load than on those non-critical loads exceeding the base load.
Publisher: Microbiology Society
Date: 2001
DOI: 10.1099/00207713-51-1-195
Abstract: Five strains of the filamentous bacterium 'Nostocoida limicola' III were successfully isolated into pure culture from s les of activated sludge biomass from five plants in Australia. 16S rRNA gene sequence analyses showed that all isolates were members of the Planctomycetales, most closely related to Isosphaera pallida, but they differed phenotypically from this species in that they did not glide and were not thermotolerant. The ultrastructure of these 'N. limicola' III isolates was also consistent with them being Planctomycetales, in that they possessed complex intracellular membrane systems compartmentalizing the cells. However, the arrangements of these intracellular membranes differed between isolates. These data confirm that 'N. limicola' III is phylogenetically unrelated to both 'N. limicola' I and 'N. limicola' II, activated sludge filamentous bacteria which share morphological features in common with 'N. limicola' III and which have been presumed historically to be the same or very similar bacteria.
Publisher: Elsevier BV
Date: 07-2017
Publisher: Elsevier BV
Date: 09-2021
Publisher: Informa UK Limited
Date: 09-2010
Publisher: Elsevier BV
Date: 06-2017
Publisher: American Society of Civil Engineers (ASCE)
Date: 03-2006
Publisher: Informa UK Limited
Date: 04-2004
Publisher: Informa UK Limited
Date: 04-1997
Publisher: Informa UK Limited
Date: 09-04-2013
Publisher: Elsevier BV
Date: 2015
Publisher: Elsevier BV
Date: 09-2019
Publisher: Elsevier BV
Date: 10-2020
Publisher: ASME International
Date: 19-05-2006
DOI: 10.1115/1.2770755
Abstract: In order to avoid high utility demand charges from cooling during the summer and to level a building’s electrical demand profile, precooling of the building’s massive structure can be applied to shift cooling-related thermal loads in response to utility pricing signals. Several previous simulation and experimental studies have shown that proper precooling can attain considerable reduction of operating cost in buildings. This paper systematically evaluates the merits of the passive building thermal capacitance to minimize energy cost for a design day using optimal control. The evaluation is conducted by means of a sensitivity analysis utilizing a dynamic building energy simulation program coupled to a popular technical computing environment. The optimal controller predicts the required extent of precooling (zone temperature set-point depression), depending on the utility rate structure, occupancy and on-peak period duration and onset, internal gains, building mass, occupancy period temperature set-point range, and weather as characterized by diurnal temperature and relative humidity swings. In addition to quantifying the building response, energy consumption, and utility cost, this paper extracts the dominant features of the optimal precooling strategies for each of the investigated cases so that guidelines for near-optimal building thermal mass savings may be developed in the future.
Publisher: Elsevier BV
Date: 09-2006
Publisher: Wiley
Date: 10-2017
Publisher: Informa UK Limited
Date: 04-2009
Publisher: MDPI AG
Date: 23-07-2019
DOI: 10.3390/SU11143988
Abstract: Buildings are widely regarded as potential sources for demand flexibility. The flexibility of thermal and electric load in buildings is a result of their interactive nature and its impact on the building’s performance. In this paper, the interaction of a building with the three interaction counterparts of the physical environment, civil infrastructure networks and other buildings is investigated. The literature review presents a wide variety of pathways of interaction and their associated potential impacts on building performance metrics such as net energy use, emissions, occupant comfort and operational cost. It is demonstrated that all of these counterparts of interaction should be considered to harness the flexibility potential of the buildings while maintaining other buildings performance metrics at a desired level. Juxtaposed with the upside potential for providing demand flexibility, numerous implementation challenges are identified that are associated with the evaluation and financial valuation of the capacity for demand flexibility, the aggregated flexibility potential, as well as the control and communication to facilitate the interactions.
Publisher: American Society of Civil Engineers (ASCE)
Date: 06-2021
Publisher: Elsevier BV
Date: 03-2022
Publisher: Informa UK Limited
Date: 04-2009
Publisher: Informa UK Limited
Date: 2009
Publisher: MDPI AG
Date: 21-08-2019
DOI: 10.3390/EN12173204
Abstract: Electric utility residential demand response programs typically reduce load a few times a year during periods of peak energy use. In the future, utilities and consumers may monetarily and environmentally benefit from continuously shaping load by alternatively encouraging or discouraging the use of electricity. One way to shape load and introduce elasticity is to broadcast forecasts of dynamic electricity prices that orchestrate electricity supply and demand in order to maximize the efficiency of conventional generation and the use of renewable resources including wind and solar energy. A binary control algorithm that influences the on and off states of end uses was developed and applied to empirical time series data to estimate price-based instantaneous opportunities for shedding and adding electric load. To overcome the limitations of traditional stochastic methods in quantifying erse, non-Gaussian, non-stationary distributions of observed appliance behaviour, recent developments in wavelet-based analysis were applied to capture and simulate time-frequency domain behaviour. The performance of autoregressive and spectral reconstruction methods was compared, with phase reconstruction providing the best simulation ensembles. Results show spatiotemporal differences in the amount of load that can be shed and added, which suggest further investigation is warranted in estimating the benefits anticipated from the wide-scale deployment of continuous automatic residential load shaping. Empirical data and documented software code are included to assist in reproducing and extending this work.
Publisher: College Publishing
Date: 11-2009
DOI: 10.3992/JGB.4.4.101
Abstract: This article describes research needs for sustainable buildings as defined in a July 2009 National Science Foundation-sponsored workshop. This workshop brought together building researchers with researchers in the areas of distributed renewable energy and multifunctional materials to engage their expertise and identify overlapping research needs and opportunities. An overview of sustainable building design provided the broad context for discussion. This overview was followed by focused presentations in building control systems, advanced building envelopes, and systems and process integration. In addition, presentations on distributed renewable energy and multi-functional materials supported the participants in outlining and generating research needs that connect the topic areas. The primary outcome from this part of the workshop was the identification of key sustainable building research needs in: transformative measurements passive strategies regional solutions and living labs systems integration storage and cascades adoption of international advances and implementation and market transformation. These needs, along with associated technical challenges and potential impacts, are described in this paper to guide sustainable building research.
Publisher: Elsevier BV
Date: 08-2021
Publisher: Informa UK Limited
Date: 09-2012
Publisher: Informa UK Limited
Date: 07-1997
Publisher: IOP Publishing
Date: 11-2019
DOI: 10.1088/1742-6596/1343/1/012058
Abstract: Buildings account for over 70% of the electricity use in the US. As cities grow, high peaks of electricity consumption are becoming more frequent, which leads to higher prices for electricity. Demand response is the coordination of electrical loads such that they react to price signals and coordinate with each other to shave the peaks of electricity consumption. We explore the use of multi-agent deep deterministic policy gradient (DDPG), an adaptive and model-free reinforcement learning control algorithm, for coordination of several buildings in a demand response scenario. We conduct our experiment in a simulated environment with 10 buildings.
Publisher: Elsevier BV
Date: 02-2018
Publisher: ASME International
Date: 11-1998
DOI: 10.1115/1.2888131
Abstract: Ice storage systems have the reputation of saving cost for operating building cooling plants by appropriately recognizing time-of-use incentives in the utility rate structure. However, many systems can consume more electrical energy than a conventional cooling plant without ice storage. This excess energy problem is illustrated in this paper by a simplified cooling plant model employed in a simulation environment that allows the assessment of the control performance of various conventional and optimal strategies. The optimal control strategy of minimizing operating cost only is introduced and subsequently is modified to allow the simultaneous consideration of operating cost and energy consumption. This proposed optimal control strategy could be valuable if ice storage systems are to stand on their own merits in a deregulated utility environment. Due to the lack of demand charges under real-time pricing, even small energy penalties and their associated excess energy cost may jeopardize the feasibility of the ice storage system.
Publisher: Informa UK Limited
Date: 10-2012
Publisher: ASME International
Date: 27-01-2003
DOI: 10.1115/1.1530629
Abstract: A simplified ice storage system model was developed in which the icemaking mode is reflected by a higher power consumption per unit cooling than in chilled-water mode. The performance of four control strategies for ice storage systems is evaluated. The four control strategies investigated are chiller-priority and constant-proportion as conventional, instantaneous controls, while storage-priority and optimal control represent sophisticated controls employing load forecasting. Six parameters were investigated with respect to their influence on the ice storage system performance: Storage losses, utility rate structures, rate periods, penalty for icemaking, storage capacity, and the impact of load forecasting. Optimal control was determined to provide maximal operating cost savings. The storage-priority control yields operating costs only slightly higher than those of optimal control. Chiller-priority control realized savings that were typically on the order of 50% of what is theoretically possible (optimal control). Constant-proportion control proved to be a simple control strategy yielding higher savings than chiller-priority, yet lower than storage-priority control.
Publisher: ASME International
Date: 31-10-2006
DOI: 10.1115/1.2710491
Abstract: This paper describes an investigation of machine learning for supervisory control of active and passive thermal storage capacity in buildings. Previous studies show that the utilization of active or passive thermal storage, or both, can yield significant peak cooling load reduction and associated electrical demand and operational cost savings. In this study, a model-free learning control is investigated for the operation of electrically driven chilled water systems in heavy-mass commercial buildings. The reinforcement learning controller learns to operate the building and cooling plant based on the reinforcement feedback (monetary cost of each action, in this study) it receives for past control actions. The learning agent interacts with its environment by commanding the global zone temperature setpoints and thermal energy storage charging∕discharging rate. The controller extracts information about the environment based solely on the reinforcement signal the controller does not contain a predictive or system model. Over time and by exploring the environment, the reinforcement learning controller establishes a statistical summary of plant operation, which is continuously updated as operation continues. The present analysis shows that learning control is a feasible methodology to find a near-optimal control strategy for exploiting the active and passive building thermal storage capacity, and also shows that the learning performance is affected by the dimensionality of the action and state space, the learning rate and several other factors. It is found that it takes a long time to learn control strategies for tasks associated with large state and action spaces.
Publisher: ASME International
Date: 02-2004
DOI: 10.1115/1.1637640
Abstract: It has been shown that a neural network with sufficient hidden units can approximate any continuous function defined on a closed and bounded set. This has inspired the use of neural networks as general nonlinear regression models. As with other nonlinear regression models, tools of conventional statistical analysis can be applied to neural networks to yield a test for the relevance or irrelevance of a free parameter. The test, a version of Wald’s test, can be extended to yield a test for the relevance or irrelevance of an input variable. This test was applied to the building energy use data of the Energy Prediction Shootout II contest. Input variables were selected by initially constructing a neural network model which had many inputs, then cutting out the inputs which were deemed irrelevant on the basis of the Wald test. Time-lagged values were included for some input variables, with the time lag chosen by inspecting the autocovariance function of the candidate variable. The results of the contest entry are summarized, and the benefits of applying Wald’s test to this problem are assessed.
Publisher: Wiley
Date: 27-02-2019
DOI: 10.1002/WENE.339
Abstract: Recent efforts to reduce energy consumption and greenhouse gas emissions have resulted in the development of sustainable, smart districts with highly energy efficient buildings, renewable distributed energy resources (DERs), and support for alternative modes of transportation. However, there is typically little if any coordination between the district developers and the local utility. Most attention is paid to the district's annual net load and generation without considering their instantaneous imbalance or the connecting network's state. This presents an opportunity to learn lessons from the design of distribution feeders for districts characterized by low loads and high penetrations of DERs that can be applied to the distribution grid at large. The aim of this overview is to summarize current practices in sustainable district planning as well as advances in modeling and design tools for incorporating the power distribution system into the district planning process. Recent developments in the modeling and optimization of district power systems, including their coordination with multi‐energy systems and the impact of high penetration levels of renewable energy, are introduced. Sustainable districts in England and Japan are reviewed as case studies to illustrate the extent to which distribution system planning has been considered in practice. Finally, newly developed building‐to‐grid modeling tools that can facilitate coordinated district and power system design with utility involvement are introduced, along with suggestions for future research directions. This article is categorized under: Wind Power Systems and Infrastructure Energy Policy and Planning Systems and Infrastructure Energy Efficiency Systems and Infrastructure
Publisher: American Society of Civil Engineers (ASCE)
Date: 06-2021
Publisher: Elsevier BV
Date: 02-2004
Publisher: Elsevier BV
Date: 12-2013
Publisher: Informa UK Limited
Date: 03-05-2013
Publisher: Elsevier BV
Date: 03-2016
Publisher: Elsevier BV
Date: 2008
Publisher: Informa UK Limited
Date: 06-2011
Publisher: MDPI AG
Date: 15-10-2020
DOI: 10.3390/EN13205396
Abstract: Occupancy-aware heating, ventilation, and air conditioning (HVAC) control offers the opportunity to reduce energy use without sacrificing thermal comfort. Residential HVAC systems often use manually-adjusted or constant setpoint temperatures, which heat and cool the house regardless of whether it is needed. By incorporating occupancy-awareness into HVAC control, heating and cooling can be used for only those time periods it is needed. Yet, bringing this technology to fruition is dependent on accurately predicting occupancy. Non-probabilistic prediction models offer an opportunity to use collected occupancy data to predict future occupancy profiles. Smart devices, such as a connected thermostat, which already include occupancy sensors, can be used to provide a continually growing collection of data that can then be harnessed for short-term occupancy prediction by compiling and creating a binary occupancy prediction. Real occupancy data from six homes located in Colorado is analyzed and investigated using this occupancy prediction model. Results show that non-probabilistic occupancy models in combination with occupancy sensors can be combined to provide a hybrid HVAC control with savings on average of 5.0% and without degradation of thermal comfort. Model predictive control provides further opportunities, with the ability to adjust the relative importance between thermal comfort and energy savings to achieve savings between 1% and 13.3% depending on the relative weighting between thermal comfort and energy savings. In all cases, occupancy prediction allows the opportunity for a more intelligent and optimized strategy to residential HVAC control.
Publisher: Informa UK Limited
Date: 22-12-2021
Publisher: Elsevier BV
Date: 03-2003
Publisher: Elsevier BV
Date: 06-2015
Publisher: American Society of Civil Engineers (ASCE)
Date: 12-0017
Publisher: Elsevier BV
Date: 2008
Publisher: Springer Science and Business Media LLC
Date: 28-10-2021
DOI: 10.1038/S41597-021-01055-X
Abstract: This paper describes development of a data acquisition system used to capture a range of occupancy related modalities from single-family residences, along with the dataset that was generated. The publicly available dataset includes: grayscale images at 32-by-32 pixels, captured every second audio files, which have undergone processing to remove personally identifiable information indoor environmental readings, captured every ten seconds and ground truth binary occupancy status. The data acquisition system, coined the mobile human presence detection (HPDmobile) system, was deployed in six homes for a minimum duration of one month each, and captured all modalities from at least four different locations concurrently inside each home. The environmental modalities are available as captured, but to preserve the privacy and identity of the occupants, images were downsized and audio files went through a series of processing steps, as described in this paper. This dataset adds to a very small body of existing data, with applications to energy efficiency and indoor environmental quality.
Publisher: Informa UK Limited
Date: 19-02-2021
Publisher: Elsevier BV
Date: 10-2014
Publisher: ASME International
Date: 08-2003
DOI: 10.1115/1.1591801
Abstract: This paper surveys past and current research of optimal control for central chilled water plants with ice thermal energy storage. The motivation for thermal energy storage in commercial buildings is provided and common operating strategies for ice storage including their implementation are presented. The concept of optimality serves as the basis for introducing the various approaches to optimal control of thermal energy storage. Optimal strategies minimizing either energy or demand costs, near-optimal rule based control minimizing total cost, comfort-based energy optimal control, and combined optimal sizing and energy cost optimal control are discussed and contrasted with mathematically non-optimal, but heuristically improved operating strategies. Fully optimal control based on perfect knowledge is introduced and subsequent developments of predictive optimal control subject to uncertain weather, load, and utility rate information illustrated. In addition, recent investigations of adaptive optimal reinforcement learning based control are presented.
Publisher: Elsevier BV
Date: 04-2016
Publisher: ASME International
Date: 05-2010
DOI: 10.1115/1.4001466
Abstract: Using a simulation and optimization environment, this paper presents advances toward near-optimal building thermal mass control derived from full factorial analyses of the important parameters influencing the passive thermal storage process for a range of buildings and climate/utility rate structure combinations. Guidelines for the application of, and expected savings from, building thermal mass control strategies that can be easily implemented and result in a significant reduction in building operating costs and peak electrical demand are sought. In response to the actual utility rates imposed in the investigated cities, fundamental insights and control simplifications are derived from those buildings deemed suitable candidates. The near-optimal strategies are derived from the optimal control trajectory, consisting of four variables, and then tested for effectiveness and validated with respect to uncertainty regarding building parameters and climate variations. Due to the overriding impact of the utility rate structure on both savings and control strategy, combined with the overwhelming ersity of utility rates offered to commercial building customers, this study cannot offer universally valid control guidelines. Nevertheless, a significant number of cases, i.e., combinations of buildings, weather, and utility rate structure, have been investigated, which offer both insights and recommendations for simplified control strategies. These guidelines represent a good starting point for experimentation with building thermal mass control for a substantial range of building types, equipments, climates, and utility rates.
Publisher: Elsevier BV
Date: 2015
Publisher: MDPI AG
Date: 13-06-2021
DOI: 10.3390/EN14123513
Abstract: Occupant behavior can significantly influence the operation and performance of buildings. Many occupant-centric key performance indicators (KPIs) rely on having accurate counts of the number of occupants in a building, which is very different to how occupancy information is currently collected in the majority of buildings today. To address this gap, the authors develop a standardized methodology for the calculation of percent space utilization for buildings, which is formulated with respect to two prevalent operational data schemas: the Brick Schema and Project Haystack. The methodology is scalable across different levels of spatial granularity and irrespective of sensor placement. Moreover, the methods are intended to make use of typical occupancy sensors that capture presence level occupancy and not counts of people. Since occupant-hours is a preferable metric to use in KPI calculations, a method to convert between percent space utilization and occupant-hours using the design occupancy for a space is also developed. The methodology is demonstrated on a small commercial office space in Boulder, Colorado using data collected between June 2018 and February 2019. A multiple linear regression is performed that shows strong evidence for a relationship between building energy consumption and percent space utilization.
Publisher: Elsevier BV
Date: 05-2018
Publisher: Elsevier BV
Date: 02-2006
Publisher: ASME International
Date: 13-11-2005
DOI: 10.1115/1.1877513
Abstract: In contrast to building energy conversion equipment, less improvement has been achieved in thermal energy distribution, storage and control systems in terms of energy efficiency and peak load reduction potential. Cooling of commercial buildings contributes significantly to the peak demand placed on an electrical utility grid and time-of-use electricity rates are designed to encourage shifting of electrical loads to off-peak periods at night and on weekends. Buildings can respond to these pricing signals by shifting cooling-related thermal loads either by precooling the building’s massive structure (passive storage) or by using active thermal energy storage systems such as ice storage. Recent theoretical and experimental work showed that the simultaneous utilization of active and passive building thermal storage inventory can save significant amounts of utility costs to the building operator, yet increased electrical energy consumption may result. The article investigates the relationship between cost savings and energy consumption associated with conventional control, minimal cost and minimal energy control, while accounting for variations in fan power consumption, chiller capacity, chiller coefficient-of-performance, and part-load performance. The model-based predictive building controller is employed to either minimize electricity cost including a target demand charge or electrical energy consumption. This work shows that buildings can be operated in a demand-responsive fashion to substantially reduce utility costs with marginal increases in overall energy consumption. In the case of energy optimal control, the reference control was replicated, i.e., if only energy consumption is of concern, neither active nor passive building thermal storage should be utilized. On the other hand, cost optimal control suggests strongly utilizing both thermal storage inventories.
Publisher: Walter de Gruyter GmbH
Date: 08-2009
Abstract: A pharmaceutical facility located in Southern Germany is experiencing a trend of growing cooling loads to be met by the chilled water plant composed of ten chillers of greatly varying cost effectiveness. With a capacity shortfall inevitable, the question arises whether to install an additional chiller or improve the utilization of the existing chillers, in particular those with low operating costs per unit cooling, through the addition of a chilled water thermal energy storage (TES) system. To provide decision support in this matter, an optimization environment was developed and validated that adopts mixed integer programming as the approach to optimizing the chiller dispatch for any load condition, while an overarching dynamic programming based optimization approach optimizes the charge/discharge strategy of the TES system. In this fashion, the chilled water plant optimization is decoupled but embedded in the TES control optimization. The approach was selected to allow for arbitrary constraints and optimization horizons, while ensuring a global optimum to the problem. The results show that a relatively small TES tank provides significant economic and operational benefits. Yet, in order to facilitate long-term supply security, a larger TES tank capacity was decided on and the TES system was constructed in 2008.
Publisher: Elsevier BV
Date: 11-2019
Publisher: American Society of Civil Engineers (ASCE)
Date: 06-2018
Publisher: American Society of Civil Engineers (ASCE)
Date: 06-2014
Publisher: SAGE Publications
Date: 06-07-2010
Abstract: This paper reviews the literature on occupancy-based lighting control as a prelude to the application of sensor networks to building management. Many buildings include systems to detect occupancy and control building services. Current systems use single measurement points to detect occupancy, and there can be significant uncertainty associated with the measurement of occupancy. Long time delay and high detector sensitivity settings compensate for this uncertainty, but these diminish the savings that could be achieved with more accurate occupancy measurement. More effective control may be provided by more extensive sensing, using a network of occupancy sensors, and more extensive analysis of sensor data. The literature reviewed in this paper establishes the need for an investigation of the performance of sensor networks when used for lighting control.
Publisher: American Society of Civil Engineers (ASCE)
Date: 09-2017
Publisher: SAGE Publications
Date: 06-08-2010
Abstract: A new occupancy detection sensor network was developed, commissioned and installed in two private offices: data were collected to evaluate the utility of the sensor network for lighting control. Results show that there is considerable uncertainty associated with the determination of occupancy using measurements from a single detector in a space. A sensor network reduces uncertainty, because data from other detectors provides converging information that can be used to determine if a space is occupied. Sophisticated analysis techniques can be applied to the sensor network data stream to provide improved occupancy measurement and lighting control, compared to current systems.
Publisher: Informa UK Limited
Date: 05-03-2012
Publisher: Informa UK Limited
Date: 09-2009
Publisher: Elsevier BV
Date: 12-2016
Publisher: Informa UK Limited
Date: 11-01-2012
Publisher: MDPI AG
Date: 06-12-2021
DOI: 10.3390/JSAN10040071
Abstract: Many regions of the world benefit from heating, ventilating, and air-conditioning (HVAC) systems to provide productive, comfortable, and healthy indoor environments, which are enabled by automatic building controls. Due to climate change, population growth, and industrialization, HVAC use is globally on the rise. Unfortunately, these systems often operate in a continuous fashion without regard to actual human presence, leading to unnecessary energy consumption. As a result, the heating, ventilation, and cooling of unoccupied building spaces makes a substantial contribution to the harmful environmental impacts associated with carbon-based electric power generation, which is important to remedy. For our modern electric power system, transitioning to low-carbon renewable energy is facilitated by integration with distributed energy resources. Automatic engagement between the grid and consumers will be necessary to enable a clean yet stable electric grid, when integrating these variable and uncertain renewable energy sources. We present the WHISPER (Wireless Home Identification and Sensing Platform for Energy Reduction) system to address the energy and power demand triggered by human presence in homes. The presented system includes a maintenance-free and privacy-preserving human occupancy detection system wherein a local wireless network of battery-free environmental, acoustic energy, and image sensors are deployed to monitor homes, record empirical data for a range of monitored modalities, and transmit it to a base station. Several machine learning algorithms are implemented at the base station to infer human presence based on the received data, harnessing a hierarchical sensor fusion algorithm. Results from the prototype system demonstrate an accuracy in human presence detection in excess of 95% ongoing commercialization efforts suggest approximately 99% accuracy. Using machine learning, WHISPER enables various applications based on its binary occupancy prediction, allowing situation-specific controls targeted at both personalized smart home and electric grid modernization opportunities.
Publisher: Informa UK Limited
Date: 04-2005
Publisher: Elsevier BV
Date: 08-2004
Publisher: Informa UK Limited
Date: 22-09-2016
Publisher: Informa UK Limited
Date: 16-09-2016
Publisher: Elsevier BV
Date: 05-2015
Publisher: Elsevier BV
Date: 11-2016
Publisher: Informa UK Limited
Date: 07-2003
Publisher: SDEWES Centre
Date: 03-2021
Publisher: Elsevier BV
Date: 02-2007
Publisher: MDPI AG
Date: 21-08-2020
DOI: 10.3390/EN13174339
Abstract: District heating and cooling (DHC) is considered one of the most sustainable technologies to meet the heating and cooling demands of buildings in urban areas. The fifth-generation district heating and cooling (5GDHC) concept, often referred to as ambient loops, is a novel solution emerging in Europe and has become a widely discussed topic in current energy system research. 5GDHC systems operate at a temperature close to the ground and include electrically driven heat pumps and associated thermal energy storage in a building-sited energy transfer station (ETS) to satisfy user comfort. This work presents new strategies for improving the operation of these energy transfer stations by means of a model predictive control (MPC) method based on recurrent artificial neural networks. The results show that, under simple time-of-use utility rates, the advanced controller outperforms a rule-based controller for smart charging of the domestic hot water (DHW) thermal energy storage under specific boundary conditions. By exploiting the available thermal energy storage capacity, the MPC controller is capable of shifting up to 14% of the electricity consumption of the ETS from on-peak to off-peak hours. Therefore, the advanced control implemented in 5GDHC networks promotes coupling between the thermal and the electric sector, producing flexibility on the electric grid.
Publisher: Informa UK Limited
Date: 06-04-2022
Publisher: Informa UK Limited
Date: 06-05-2018
Publisher: Elsevier BV
Date: 08-2011
Publisher: Informa UK Limited
Date: 14-02-2014
Publisher: American Society of Civil Engineers
Date: 05-04-2013
Publisher: MDPI AG
Date: 02-2020
DOI: 10.3390/EN13030613
Abstract: This paper evaluates the potential for automated lighting control as a resource for frequency regulation of the electric grid system in the context of current energy policies, economic incentives, and technological trends. The growing prevalence of renewable energy has increased the need for ancillary services to maintain grid frequency and stability. While demand side resources like heating, ventilating, and air-conditioning systems, as well as water treatment plants are already evaluated as regulation service providers, the potential application to electrical lighting systems has largely been ignored. Yet, aggregations of lighting systems that are retrofitted with intelligent controls could conceivably contribute to frequency regulation services with little impact on user comfort. To further explore the feasibility of lighting potential, this paper explores (1) how lighting control systems are limited by visual comfort perception and acceptability, (2) how such limitations impact the performance of the lighting system as an frequency regulation resource, and (3) how the market potential of lighting systems as demand side resources compares in different regional transmission organizations. Finally, the impact of developing technologies on the application of lighting systems for frequency regulation is discussed.
Location: Australia
Location: United States of America
No related grants have been discovered for GREGOR HENZE.