The Experts below are selected from a list of 12471 Experts worldwide ranked by ideXlab platform
Yacine Rezgui - One of the best experts on this subject based on the ideXlab platform.
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Building Energy Metering and environmental monitoring - A state-of-the-art review and directions for future research
Energy and Buildings, 2016Co-Authors: Muhammad Waseem Ahmad, David Mundow, Mario Sisinni, Monjur Mourshed, Yacine RezguiAbstract:Buildings are responsible for 40% of global Energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce Energy consumption and associated greenhouse gas emissions from buildings has acted as a catalyst in the increasing installation of meters and sensors for monitoring Energy use and indoor environmental conditions in buildings. This paper reviews the state-of-the-art in building Energy Metering and environmental monitoring, including their social, economic, environmental and legislative drivers. The integration of meters and sensors with existing building Energy management systems (BEMS) is critically appraised, especially with regard to communication technologies and protocols such as ModBus, M-Bus, Ethernet, Cellular, ZigBee, WiFi and BACnet. Findings suggest that Energy Metering is covered in existing policies and regulations in only a handful of countries. Most of the legislations and policies on Energy Metering in Europe are in response to the Energy Performance of Buildings Directive (EPBD), 2002/91/EC. However, recent developments in policy are pointing towards more stringent Metering requirements in future, moving away from voluntary to mandatory compliance. With regards to Metering equipment, significant developments have been made in the recent past on miniaturisation, accuracy, robustness, data storage, ability to connect using multiple communication protocols, and the integration with BEMS and the Cloud - resulting in a range of available solutions, selection of which can be challenging. Developments in communication technologies, in particular in low-power wireless such as ZigBee and Bluetooth LE (BLE), are enabling cost-effective machine to machine (M2M) and internet of things (IoT) implementation of sensor networks. Privacy and data protection, however, remain a concern for data aggregators and end-users. The standardization of network protocols and device functionalities remains an active area of research and development, especially due to the prevalence of many protocols in the BEMS industry. Available solutions often lack interoperability between hardware and software systems, resulting in vendor lock-in. The paper provides a comprehensive understanding of available technologies for Energy Metering and environmental monitoring; their drivers, advantages and limitations; factors affecting their selection and future directions of research and development - for use a reference, as well as for generating further interest in this expanding research area.
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Building Energy Metering and environmental monitoring – A state-of-the-art review and directions for future research
Energy and Buildings, 2016Co-Authors: Muhammad Waseem Ahmad, David Mundow, Mario Sisinni, Monjur Mourshed, Yacine RezguiAbstract:Buildings are responsible for 40% of global Energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce Energy consumption and associated greenhouse gas emissions from buildings has acted as a catalyst in the increasing installation of meters and sensors for monitoring Energy use and indoor environmental conditions in buildings. This paper reviews the state-of-the-art in building Energy Metering and environmental monitoring, including their social, economic, environmental and legislative drivers. The integration of meters and sensors with existing building Energy management systems (BEMS) is critically appraised, especially with regard to communication technologies and protocols such as ModBus, M-Bus, Ethernet, Cellular, ZigBee, WiFi and BACnet. Findings suggest that Energy Metering is covered in existing policies and regulations in only a handful of countries. Most of the legislations and policies on Energy Metering in Europe are in response to the Energy Performance of Buildings Directive (EPBD), 2002/91/EC. However, recent developments in policy are pointing towards more stringent Metering requirements in future, moving away from voluntary to mandatory compliance. With regards to Metering equipment, significant developments have been made in the recent past on miniaturisation, accuracy, robustness, data storage, ability to connect using multiple communication protocols, and the integration with BEMS and the Cloud – resulting in a range of available solutions, selection of which can be challenging. Developments in communication technologies, in particular in low-power wireless such as ZigBee and Bluetooth LE (BLE), are enabling cost-effective machine to machine (M2M) and internet of things (IoT) implementation of sensor networks. Privacy and data protection, however, remain a concern for data aggregators and end-users. The standardization of network protocols and device functionalities remains an active area of research and development, especially due to the prevalence of many protocols in the BEMS industry. Available solutions often lack interoperability between hardware and software systems, resulting in vendor lock-in. The paper provides a comprehensive understanding of available technologies for Energy Metering and environmental monitoring; their drivers, advantages and limitations; factors affecting their selection and future directions of research and development – for use a reference, as well as for generating further interest in this expanding research area.
Stephen L Locke - One of the best experts on this subject based on the ideXlab platform.
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what can we learn from high frequency appliance level Energy Metering results from a field experiment
Energy Policy, 2015Co-Authors: Victor Chen, Magali A Delmas, William J Kaiser, Stephen L LockeAbstract:© 2014 Elsevier Ltd. This study uses high-frequency appliance-level electricity consumption data for 124 apartments over 24 months to provide a better understanding of appliance-level electricity consumption behavior. We conduct our analysis in a standardized set of apartments with similar appliances, which allows us to identify behavioral differences in electricity use. The Results show that households' estimations of appliance-level consumption are inaccurate and that they overestimate lighting use by 75% and underestimate plug-load use by 29%. We find that similar households using the same major appliances exhibit substantial variation in appliance-level electricity consumption. For example, households in the 75th percentile of HVAC usage use over four times as much electricity as a user in the 25th percentile. Additionally, we show that behavior accounts for 25-58% of this variation. Lastly, we find that replacing the existing refrigerator with a more Energy-efficient model leads to overall Energy savings of approximately 11%. This is equivalent to results from behavioral interventions targeting all appliances but might not be as cost effective. Our findings have important implications for behavior-based Energy conservation policies.
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what can we learn from high frequency appliance level Energy Metering results from a field experiment
Social Science Research Network, 2014Co-Authors: Victor Chen, Magali A Delmas, Stephen L Locke, William J KaiserAbstract:This study uses hourly appliance-level electricity consumption data for 124 apartments over 24 months to provide a better understanding of appliance-level electricity consumption behavior. We conduct our analysis in a standardized set of apartments with similar appliances, which allows us to identify behavioral differences in electricity use. The results show that households’ estimations of appliance-level consumption are inaccurate and that they overestimate lighting use by 60% and underestimate HVAC and plug-load by 40%. We find that similar households using the same major appliances exhibit substantial variation in appliance-level electricity consumption. Additionally, we show that behavior accounts for 25-58% of this variation. Lastly, we find that replacing the existing refrigerator with a more Energy-efficient model leads to overall Energy savings of approximately 11%, which is equivalent to results from behavioral interventions targeting all appliances but might not be as cost effective. Our findings have important implications for behavior-based Energy conservation policies.
Magali A Delmas - One of the best experts on this subject based on the ideXlab platform.
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nonprice incentives and Energy conservation
Proceedings of the National Academy of Sciences of the United States of America, 2015Co-Authors: Omar Isaac Asensio, Magali A DelmasAbstract:In the electricity sector, Energy conservation through technological and behavioral change is estimated to have a savings potential of 123 million metric tons of carbon per year, which represents 20% of US household direct emissions in the United States. In this article, we investigate the effectiveness of nonprice information strategies to motivate conservation behavior. We introduce environment and health-based messaging as a behavioral strategy to reduce Energy use in the home and promote Energy conservation. In a randomized controlled trial with real-time appliance-level Energy Metering, we find that environment and health-based information strategies, which communicate the environmental and public health externalities of electricity production, such as pounds of pollutants, childhood asthma, and cancer, outperform monetary savings information to drive behavioral change in the home. Environment and health-based information treatments motivated 8% Energy savings versus control and were particularly effective on families with children, who achieved up to 19% Energy savings. Our results are based on a panel of 3.4 million hourly appliance-level kilowatt–hour observations for 118 residences over 8 mo. We discuss the relative impacts of both cost-savings information and environmental health messaging strategies with residential consumers.
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what can we learn from high frequency appliance level Energy Metering results from a field experiment
Energy Policy, 2015Co-Authors: Victor Chen, Magali A Delmas, William J Kaiser, Stephen L LockeAbstract:© 2014 Elsevier Ltd. This study uses high-frequency appliance-level electricity consumption data for 124 apartments over 24 months to provide a better understanding of appliance-level electricity consumption behavior. We conduct our analysis in a standardized set of apartments with similar appliances, which allows us to identify behavioral differences in electricity use. The Results show that households' estimations of appliance-level consumption are inaccurate and that they overestimate lighting use by 75% and underestimate plug-load use by 29%. We find that similar households using the same major appliances exhibit substantial variation in appliance-level electricity consumption. For example, households in the 75th percentile of HVAC usage use over four times as much electricity as a user in the 25th percentile. Additionally, we show that behavior accounts for 25-58% of this variation. Lastly, we find that replacing the existing refrigerator with a more Energy-efficient model leads to overall Energy savings of approximately 11%. This is equivalent to results from behavioral interventions targeting all appliances but might not be as cost effective. Our findings have important implications for behavior-based Energy conservation policies.
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what can we learn from high frequency appliance level Energy Metering results from a field experiment
Social Science Research Network, 2014Co-Authors: Victor Chen, Magali A Delmas, Stephen L Locke, William J KaiserAbstract:This study uses hourly appliance-level electricity consumption data for 124 apartments over 24 months to provide a better understanding of appliance-level electricity consumption behavior. We conduct our analysis in a standardized set of apartments with similar appliances, which allows us to identify behavioral differences in electricity use. The results show that households’ estimations of appliance-level consumption are inaccurate and that they overestimate lighting use by 60% and underestimate HVAC and plug-load by 40%. We find that similar households using the same major appliances exhibit substantial variation in appliance-level electricity consumption. Additionally, we show that behavior accounts for 25-58% of this variation. Lastly, we find that replacing the existing refrigerator with a more Energy-efficient model leads to overall Energy savings of approximately 11%, which is equivalent to results from behavioral interventions targeting all appliances but might not be as cost effective. Our findings have important implications for behavior-based Energy conservation policies.
William J Kaiser - One of the best experts on this subject based on the ideXlab platform.
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what can we learn from high frequency appliance level Energy Metering results from a field experiment
Energy Policy, 2015Co-Authors: Victor Chen, Magali A Delmas, William J Kaiser, Stephen L LockeAbstract:© 2014 Elsevier Ltd. This study uses high-frequency appliance-level electricity consumption data for 124 apartments over 24 months to provide a better understanding of appliance-level electricity consumption behavior. We conduct our analysis in a standardized set of apartments with similar appliances, which allows us to identify behavioral differences in electricity use. The Results show that households' estimations of appliance-level consumption are inaccurate and that they overestimate lighting use by 75% and underestimate plug-load use by 29%. We find that similar households using the same major appliances exhibit substantial variation in appliance-level electricity consumption. For example, households in the 75th percentile of HVAC usage use over four times as much electricity as a user in the 25th percentile. Additionally, we show that behavior accounts for 25-58% of this variation. Lastly, we find that replacing the existing refrigerator with a more Energy-efficient model leads to overall Energy savings of approximately 11%. This is equivalent to results from behavioral interventions targeting all appliances but might not be as cost effective. Our findings have important implications for behavior-based Energy conservation policies.
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what can we learn from high frequency appliance level Energy Metering results from a field experiment
Social Science Research Network, 2014Co-Authors: Victor Chen, Magali A Delmas, Stephen L Locke, William J KaiserAbstract:This study uses hourly appliance-level electricity consumption data for 124 apartments over 24 months to provide a better understanding of appliance-level electricity consumption behavior. We conduct our analysis in a standardized set of apartments with similar appliances, which allows us to identify behavioral differences in electricity use. The results show that households’ estimations of appliance-level consumption are inaccurate and that they overestimate lighting use by 60% and underestimate HVAC and plug-load by 40%. We find that similar households using the same major appliances exhibit substantial variation in appliance-level electricity consumption. Additionally, we show that behavior accounts for 25-58% of this variation. Lastly, we find that replacing the existing refrigerator with a more Energy-efficient model leads to overall Energy savings of approximately 11%, which is equivalent to results from behavioral interventions targeting all appliances but might not be as cost effective. Our findings have important implications for behavior-based Energy conservation policies.
Prabal Dutta - One of the best experts on this subject based on the ideXlab platform.
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Energy harvesting thermoelectric sensing for unobtrusive water and appliance Metering
Proceedings of the 2nd International Workshop on Energy Neutral Sensing Systems, 2014Co-Authors: Bradford Campbell, Branden Ghena, Prabal DuttaAbstract:Fine-grained Energy Metering in homes and buildings provides a promising technique for addressing the unmaintainable Energy consumption levels of worldwide buildings. Metering electricity, lighting, natural gas, HVAC, occupancy, and water on a per appliance or room basis can provide invaluable insight when trying to reduce a building's Energy footprint. A myriad of sensor designs and systems collect data on particular building aspects, but are often hampered by installation difficulty or ongoing maintenance needs (like battery replacement). We address these common pitfalls for water and heat Metering by developing a small, Energy-harvesting sensor that meters using the same thermoelectric generator with which it powers itself. In short, the rate at which the harvester captures Energy is proportional to the heat production of the monitored appliance or pipe and this relationship allows us to estimate Energy use simply based on the sensor's ability to harvest. We prototype our sensor in a bracelet shaped form-factor that can attach to a shower head pipe, faucet, or appliance to provide local hot water or heat Metering.
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monjolo an Energy harvesting Energy meter architecture
International Conference on Embedded Networked Sensor Systems, 2013Co-Authors: Samuel Debruin, Bradford Campbell, Prabal DuttaAbstract:Conventional AC power meters perform at least two distinct functions: power conversion, to supply the meter itself, and Energy Metering, to measure the load consumption. This paper presents Monjolo, a new Energy-Metering architecture that combines these two functions to yield a new design point in the Metering space. The key insight underlying this work is that the output of a current transformer -- nominally used to measure a load current -- can be harvested and used to intermittently power a wireless sensor node. The hypothesis is that the node's activation frequency increases monotonically with the primary load's draw, making it possible to estimate load power from the interval between activations, assuming the node consumes a fixed Energy quanta during each activation. This paper explores this thesis by designing, implementing, and evaluating the Monjolo Metering architecture. The results demonstrate that it is possible to build a meter that draws zero-power under zero-load conditions, offers high accuracy for near-unity power factor loads, works with non-unity power factor loads in combination with a whole-house meter, wirelessly reports readings to a data aggregator, is resilient to communication failures, and is parsimonious with the radio channel, even under heavy loads. Monjolo eliminates the high-voltage AC-DC power supply and AC Metering circuitry present in earlier designs, enabling a smaller, simpler, safer, and lower-cost design point that supports novel deployment scenarios like non-intrusive circuit-level Metering.
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design and implementation of a high fidelity ac Metering network
Information Processing in Sensor Networks, 2009Co-Authors: Xiaofan Jiang, Prabal Dutta, Stephen Dawsonhaggerty, David E CullerAbstract:We present the architecture, design, and preliminary evaluation of ACme, a wireless sensor and actuator network for monitoring AC Energy usage and controlling AC devices in a large and diverse building environment. The ACme system consists of three tiers: the ACme node which provides a Metering and control interface to a single outlet, a network fabric which allows this interface to be exported to arbitrary IP endpoints, and application software that uses this networked interface to provide various power-centric applications. The ACme node integrates an Epic core module with a dedicated Energy Metering IC to provide real, reactive, and apparent power measurements, with optional control of an attached load. The network comprises a complete IPv6/6LoWPAN stack on every node and an edge router that connects to other IP networks. The application tier receives and stores readings in a database and uses a web server for visualization. Nodes automatically join the IPv6 subnet after being plugged in, and begin interactions with the application layer. We evaluate our system in a preliminary green building deployment with 49 nodes spread over several floors of a Computer Science Building and present Energy consumption data from this preliminary deployment.
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Energy Metering for free: Augmenting switching regulators for real-time monitoring
Proceedings - 2008 International Conference on Information Processing in Sensor Networks IPSN 2008, 2008Co-Authors: Prabal Dutta, Mark Feldmeier, Joseph Paradiso, David CullerAbstract:We present iCount, a new Energy meter design. For many systems that have a built-in switching regulator, adding a single wire between the regulator and the microcontroller enables real-time Energy Metering. iCount measures Energy usage by counting the switching cycles of the regulator. We show that the relationship between load current and switching frequency is quite linear and demonstrate that this simple design can be applied to a variety of regulators. Our particular implementation exhibits a maximum error of less than plusmn20% over five decades of current draw, a resolution exceeding 1 muJ, a read latency of 15 mus, and a power overhead that ranges from 1% when the node is in standby to 0.01 % when the node is active, for a typical workload. The basic iCount design requires only a pulse frequency modulated switching regulator and a microcontroller with an externally-clocked counter.
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Quanto: Tracking Energy in networked embedded systems
Proceedings of the 8th USENIX Symposium on Operating Systems Design and Implementation (OSDI'08), 2008Co-Authors: Rodrigo Fonseca, Philip Levis, Prabal Dutta, Ion StoicaAbstract:We present Quanto, a network-wide time and Energy profiler for embedded network devices. By combining well-defined interfaces for hardware power states, fast high-resolution Energy Metering, and causal tracking of programmer-defined activities, Quanto can map how en- ergy and time are spent on nodes and across a network. Implementing Quanto on the TinyOS operating system required modifying under 350 lines of code and adding 1275 new lines. We show that being able to take fine- grained Energy consumption measurements as fast as reading a counter allows developers to precisely quan- tify the effects of low-level system implementation deci- sions, such as using DMA versus direct bus operations, or the effect of external interference on the power draw of a low duty-cycle radio. Finally, Quanto is lightweight enough that it has a minimal effect on system behavior: each sample takes 100 CPU cycles and 12 bytes of RAM.