The Experts below are selected from a list of 111 Experts worldwide ranked by ideXlab platform
Vivian Loftness - One of the best experts on this subject based on the ideXlab platform.
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Occupant behavior and schedule prediction based on office applinace Energy consumption data mining
Energy and Buildings, 2014Co-Authors: Jian Zhao, Bertrand Lasternas, Ray Yun, K P Lam, A Aziz, Huaiyuan Wang, Vivian LoftnessAbstract:Plug load and Heating Ventilation and Air Conditioning (HVAC) systems are the two largest Energy consumers in commercial buildings. The use of the 2 systems is closely related to occupant behavior and schedule. It is significant to learn the occupant behavior pattern and predict occupancy schedule for controlling the systems to save Energy. A data mining study is performed on the office Appliance Energy consumption data to predict the individual occupant behavior and the occupancy schedule in an open office space. An experiment is conducted for 2 weeks by using wireless electric outlet meters. The 5-minute interval electricity consumption data of computers, computer monitors, task lights, and other office Appliances are monitored for 6 office workers. Occupant behavior is categorized as " Occupied computer-based work " , " Occupied non-computer-based work " , " Unoccupied remote work " , and " Unoccupied " . C4.5 algorithm is used for pattern recognition over the Appliance electricity consumption data individually. The average percentage of correct of the 6 individuals is 92.39% using 10-fold cross validation. The occupancy schedule for the space is predicted by using total Energy consumption of each subject with Linear Regression algorithm. The correlation coefficient is 0.92 using 10-fold cross validation. The results suggest the models are feasible and can be applied to the plug load and HVAC control systems to reduce Energy consumptions.
Fathi Abugchem - One of the best experts on this subject based on the ideXlab platform.
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heuristic optimization of consumer electricity costs using a generic cost model
Energies, 2015Co-Authors: Chris Ogwumike, Michael Short, Fathi AbugchemAbstract:Many new demand response strategies are emerging for Energy management in smart grids. Real-Time Energy Pricing (RTP) is one important aspect of consumer Demand Side Management (DSM), which encourages consumers to participate in load scheduling. This can help reduce peak demand and improve power system efficiency. The use of Intelligent Decision Support Systems (IDSSs) for load scheduling has become necessary in order to enable consumers to respond to the changing economic value of Energy across different hours of the day. The type of scheduling problem encountered by a consumer IDSS is typically NP-hard, which warrants the search for good heuristics with efficient computational performance and ease of implementation. This paper presents an extensive evaluation of a heuristic scheduling algorithm for use in a consumer IDSS. A generic cost model for hourly pricing is utilized, which can be configured for traditional on/off peak pricing, RTP, Time of Use Pricing (TOUP), Two-Tier Pricing (2TP) and combinations thereof. The heuristic greedily schedules controllable Appliances to minimize smart Appliance Energy costs and has a polynomial worst-case computation time. Extensive computational experiments demonstrate the effectiveness of the algorithm and the obtained results indicate the gaps between the optimal achievable costs are negligible.
William J Knottenbelt - One of the best experts on this subject based on the ideXlab platform.
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the uk dale dataset domestic Appliance level electricity demand and whole house demand from five uk homes
Scientific Data, 2015Co-Authors: Jack Kelly, William J KnottenbeltAbstract:Many countries are rolling out smart electricity meters. These measure a home’s total power demand. However, research into consumer behaviour suggests that consumers are best able to improve their Energy efficiency when provided with itemised, Appliance-by-Appliance consumption information. Energy disaggregation is a computational technique for estimating Appliance-by-Appliance Energy consumption from a whole-house meter signal. To conduct research on disaggregation algorithms, researchers require data describing not just the aggregate demand per building but also the ‘ground truth’ demand of individual Appliances. In this context, we present UK-DALE: an open-access dataset from the UK recording Domestic Appliance-Level Electricity at a sample rate of 16 kHz for the whole-house and at 1/6 Hz for individual Appliances. This is the first open access UK dataset at this temporal resolution. We recorded from five houses, one of which was recorded for 655 days, the longest duration we are aware of for any Energy dataset at this sample rate. We also describe the low-cost, open-source, wireless system we built for collecting our dataset. Machine-accessible metadata file describing the reported data (ISA-Tab format)
Martin Kumar Patel - One of the best experts on this subject based on the ideXlab platform.
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dsm interactions what is the impact of Appliance Energy efficiency measures on the demand response peak load management
Energy Policy, 2020Co-Authors: Selin Yilmaz, A Rinaldi, Martin Kumar PatelAbstract:Abstract: To date, research has mostly focused on the impact of Energy efficiency on the total electricity demand but not on the electricity demand profiles. To address this gap, we estimate the impact of Energy efficiency measures and policies such as minimum Energy performance standards on the peak load by developing a bottom-up model that generates Swiss household hourly electricity demand profiles per Appliance based on time use data. The model estimates that evening Appliance peak demand can be reduced by 38% when the Appliances are replaced by the highest Energy efficiency label available on market. We find that changing light bulbs to LED would have the same peak reduction as switching cooking or wet Appliances to off-peak periods throughout the year. We also show that the evening Appliance peak demand could reduce in 2035 by 24% thanks to the improvement of the Energy performance of the stock. Cooking Appliances, the least favourable Appliances to be involved in demand response, is expected to be the highest contributors to the evening peak in 2035. Our findings show that policy makers should pay due attention to Energy efficiency improvement not only for reducing electricity demand but also in order to reduce peak load.
Martin Fischer - One of the best experts on this subject based on the ideXlab platform.
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ranking Appliance Energy efficiency in households utilizing smart meter data and Energy efficiency frontiers to estimate and identify the determinants of Appliance Energy efficiency in residential buildings
Energy and Buildings, 2015Co-Authors: Amir Kavousian, Ram Rajagopal, Martin FischerAbstract:Abstract This paper offers a novel method to rank residential Appliance Energy efficiency utilizing Energy efficiency frontiers. The method is validated using a real-world case study of 4231 buildings in Ireland. Our results show that structural factors have the largest impact on Energy efficiency, followed by socioeconomic factors and behavioral factors. For example, households with high penetration of efficient lightbulbs and double-glazed windows were on average 4 and 3.5% more efficient than others. Households with the head of household having higher education are on average 1.3% more efficient than their peers. Finally, households that track their Energy savings are on average 0.4% more efficient than others. Furthermore, installing heater timers, wall insulation, and living in owned residences were correlated with higher efficiency. Generally, families with kids who have full-time employment and are highly-educated are more efficient compared to families with no kids, or families with retirees or unemployed members. This result has important implications for both targeting and messaging of Energy efficiency programs. Some behavioral factors demonstrated significant impact on Appliance Energy efficiency. For instance, households that expressed interest in making major Energy-saving lifestyle changes scored higher efficiency ranks on average. Conversely, households that expressed doubt about their motivation to save Energy ranked lower in efficiency. This finding validates the role of educational programs to increase awareness about Energy efficiency and its importance. In short, our results show that a data-driven analysis of a population is needed to develop a balanced view of the drivers of Energy efficiency, and to devise a targeted approach to improve homes’ Energy efficiency.