Calibrating a combined energy systems analysis and controller design method with empirical data

Murphy, Gavin Bruce; Counsell, John; Allison, John; Brindley, J.

doi:10.1016/j.energy.2013.06.015

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…In the simulation, the supplying of heat was switched between operating ASHP and QB based electricity tariff for utilising off peak electricity [4]. In this paper, for the same system schedule running on E7 & TOU tariffs, the calibrated model [4,6,13] of the RISE heating system is used to analyse its performance in terms of the current dynamic state of UK power grid caused by renewable power generation resulting in dynamic carbon intensity for realistic Green House Gas (GHG) Emissions.…”

Section: Hybrid Electric Heat Pump Systemmentioning

confidence: 99%

Comparative Performance Modelling of Heat Pump based Heating Systems using Dynamic Carbon Intensity

Counsell¹,

Khalid²,

Stewart³

2018

11th IET International Conference on Advances in Power System Control, Operation and Management (APSCOM 2018)

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Modern buildings and homes utilise multiple systems for energy generation, supply and storage in order to maintain occupant comfort, reduce operating costs and CO2 emissions. In recent times electricity generation and supply network (UK National Grid) have had to manage variable supply from renewable sources such as wind turbines and photovoltaics. This resulting supply mixture has a dynamic profile at intermittent times. To manage excess supply, the options are either to reduce the generation by power stations/renewables or reinforce the power infrastructure with storage capability. This has given rise to calls for electrification of services in streamlining the supply profile through intelligent demand response such as electric heating and vehicles. Furthermore, due to zero carbon energy sources with dynamic supply profile, the carbon intensity is no longer constant. This impacts the seasonal CO2 emissions calculations and also the design and performance of electrical powered heat pump based heating systems.The RISE (Renewable Integrated Sustainable Electric) heating system was developed (funded by the UK Research and Innovation), where an electrical powered Heat pump is combined with electric thermal storage allowing low cost and low carbon electricity to be utilised. For such a system more realistic performance analysis requires dynamic carbon intensity calculations to assess impact on its ability to maintain comfort, low operating costs and low carbon emissions. The paper builds upon previously published research on the RISE system comparing with domestic Gas Condensing Boiler (GCB) using static carbon calculations. This paper presents a comparison between the RISE system and standard domestic electrical powered Air Source Heat Pump (ASHP) when using static and dynamic carbon intensity profiles. The Inverse Dynamics based Energy Assessment and Simulation (IDEAS) framework is utilised for modelling and dynamic simulation of building and heating system, operating cost and estimation of annual emissions based on half hourly (HH) dynamic CO2 intensity figures rather than annual average.The results show that with dynamic carbon intensity calculations, both electric heat pump based heating systems, RISE and ASHP show a significant increase (>15%) in carbon emissions for space heating. The results also show that whilst the RISE system's thermal storage helps to reduce running costs using a time of use tariff (TOU), it only provides a small benefit in carbon emissions.

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Section: Hybrid Electric Heat Pump Systemmentioning

confidence: 99%

Comparative Performance Modelling of Heat Pump based Heating Systems using Dynamic Carbon Intensity

Counsell¹,

Khalid²,

Stewart³

2018

11th IET International Conference on Advances in Power System Control, Operation and Management (APSCOM 2018)

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“…Initial attempts to develop self-calibrating energy models began around the early 1980s. Many such efforts can be found for individual simulation tools, such as ESP-r [20] while others propose multi-engine frameworks to calibration for sub-components of a building's dynamics [21]. It has also been applied for calibrating calibrating residential energy user to regional data while explicitly taking into account uncertainty [22].…”

Section: Tuning Of Building Modelsmentioning

confidence: 99%

Scalable tuning of building models to hourly data

Garrett

2015

Energy

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Energy models of existing buildings are unreliable unless calibrated so that they correlate well with actual energy usage. Manual tuning requires a skilled professional, is prohibitively expensive for buildings below 50,000 ft 2 , imperfect, non-repeatable, and not scalable to the dozens of sensor channels that smart meters, smart appliances, and sensors are making available. A scalable, automated methodology is needed to quickly, intelligently calibrate building energy models to all available data, increase the usefulness of those models, and facilitate speed-and-scale penetration of simulation-enhanced capabilities into the marketplace for actualized energy savings. The "Autotune" project is a novel, model-agnostic methodology that leverages supercomputing, large simulation ensembles, and big data mining with multiple machine learning algorithms to allow automatic calibration of simulations that match measured experimental data in a way that is deployable on commodity hardware. This paper shares several methodologies employed to reduce the combinatorial complexity to a computationally tractable search problem for hundreds of input parameters. Accuracy metrics are provided that quantify model error to measured data from a highly instrumented, emulated-occupancy research home.

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