Simulating energy use and energy pricing in buildings: The case of electricity

Kouveletsou, Maria; Sakkas, Nikos; Garvin, Stephen; Batić, Marko; Reccardo, Daniela; Sterling, Raymond

doi:10.1016/j.enbuild.2012.07.031

Cited by 15 publications

(7 citation statements)

References 13 publications

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“…The results were published by the candidate in a journal paper [3]. 4. Energy demand modelling tool used to determine demand profile suitable for utilization by energy planning tools.…”

Section: Achieved Scientific Resultsmentioning

confidence: 99%

Software system for multi-criteria planning and management of hybrid microgrids

Batić

Vraneš

2019

2019 27th Telecommunications Forum (TELFOR)

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Efficient operation and management of energy systems in the context of increasing penetration of distributed resources and renewable energy sources combined with energy storages, gird-tied configurations and deregulated energy market context pose a complex energy-dispatching problem, the one conventional energy management systems are not able to cope with. This complexity also hinders faster adoption of renewables and aggravates their cost effectiveness. This thesis proposes an innovative software system, which brings advanced microgrid planning, and operation algorithms while leveraging existing supervision and control systems to collect data and information from the field level but also to forward desired control action. Moreover, the thesis also proposes innovative approach for seamless integration with diverse, legacy, ICT systems thus contributing to system large-scale applicability. Following is a breakdown of concrete results and outputs with emphasis on their specific advancements comparted to existing approaches. Similarly, to existing approaches the underlying methodology of microgrid planning tool considers hybrid energy infrastructures with both conventional and renewable energy sources, converters and storages, featured by their mathematical representation. However, proposed methodology advances existing tools and planning approaches by simultaneously considering both electric and thermal energy domain, by considering both grid-tied and isolated deployment scenarios and, foremost, by leveraging demand side flexibility for optimized dimensioning of energy assets.Moreover, multi-criteria decision-making algorithms are used to rank multiple feasible planning alternatives. For the purpose of determining expected energy demand profile, an approach to leverage user habits and behaviour together with characteristics of existing energy infrastructure, rather than building physics, was developed.The developed microgrid management tool aimed at providing additional intelligence to existing supervision and control systems resulting in the cost savings typically exceeding 10%, as demonstrated in a real world use case, at the cost of ICT system retrofit. The achieved level of cost savings is comparable to those of retrofit of energy assets, which require considerable higher investments. The underlying microgrid management methodology complements existing approaches that optimize energy imports for a desired demand and available storages, by introducing appliance-level demand response actions, which can be immediately translated into control actions. Lastly, an ontology-based facility model of complex facilities and corresponding energy infrastructure was developed together with unified messaging format to enable semantic interoperability of existing diverse, legacy, supervision and control systems, which use proprietary communication protocols. By employing semantic web technologies and leveraging existing models and standards, the developed model stores contextual knowledge about the entire facility, and not jus...

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“…The results were published by the candidate in a journal paper [3]. 4. Energy demand modelling tool used to determine demand profile suitable for utilization by energy planning tools.…”

Section: Achieved Scientific Resultsmentioning

confidence: 99%

Software system for multi-criteria planning and management of hybrid microgrids

Batić

Vraneš

2019

2019 27th Telecommunications Forum (TELFOR)

Self Cite

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“…Kouveletsou et al [23] developed an electricity consumption predictor for 1 h and 24 h ahead prediction based on a non-linear auto-regressive neural network (NARX). Such a method or similar ones could be used to predict the overall energy use of the building for plug loads and lighting, thus making it possible to give a reliable estimation of the electric energy use due to non-HVAC systems and predict the building total consumption of electricity as well as the amount of possible energy exchange between the building and the smart grid.…”

Section: State Of the Art And Placement Of The Present Researchmentioning

confidence: 99%

“…Finally, for the prediction of the electricity power profiles due to lighting and plug loads, the model by Kouveletsou et al [23] can be used too. Kouveletsou et al [23] developed an electricity consumption predictor for 1 h and 24 h ahead prediction based on a non-linear auto-regressive neural network (NARX).…”

Section: State Of the Art And Placement Of The Present Researchmentioning

confidence: 99%

Demand response management by means of heat pumps controlled via real time pricing

Schibuola

Scarpa

Tambani

2015

Energy and Buildings

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“…DHW energy use profiles are the primary instrument for understanding the process of DHW energy use in the buildings [12]. Analysis of DHW energy use profiles shows the changes in energy use in different time intervals [13]. The profiles of DHW energy use allow us to determine the hours of peak energy loads and other energy load characteristics of a building.…”

Section: Introductionmentioning

confidence: 99%

Identifying typical hourly DHW energy use profiles in a hotel in Norway by using statistical methods

et al. 2019

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The aim of this research is to improve the existing approaches of domestic hot water (DHW) energy use analysis in buildings. A comprehensive statistical analysis of hourly DHW energy use for a hotel in Oslo, Norway, was performed. To recognize the trend of DHW energy use over several years, Centered Moving Average method was applied. To increase the accuracy of DHW energy use analysis, it was proposed to identify the months and days of the week with similar characteristics of DHW energy use and build unified profiles for them. For this purpose, the approaches based on the student’s t-tests and Fisher’s test was proposed. The analysis allowed us to detect two seasons of DHW energy use. In addition, it was revealed that behavior of DHW energy use on Mondays significantly different from other working days. To recognize the timing of peak and average and low DHW energy use, method of statistical grouping of the hourly energy use was utilized. The typical profiles of DHW energy in the hotel were obtained. The profiles proposed in the present article more reliably reflect the regimes of DHW energy use in the hotel and take into account factors that have influence on DHW use.

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Simulating energy use and energy pricing in buildings: The case of electricity

Cited by 15 publications

References 13 publications

Software system for multi-criteria planning and management of hybrid microgrids

Software system for multi-criteria planning and management of hybrid microgrids

Demand response management by means of heat pumps controlled via real time pricing

Identifying typical hourly DHW energy use profiles in a hotel in Norway by using statistical methods

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