Smart Thermal Grids – A Review

Stănişteanu, Cristina

doi:10.1515/sbeef-2016-0030

Cited by 9 publications

(2 citation statements)

References 4 publications

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“…Moreover, due to service level requirements of grid infrastructure and expansion of smart meters, vast amounts of detailed and high-quality data is available in this sector [5]. However, the authors expect an increasing number of smart metering devices also in the gas and heat grids, which will improve data availability in the future and facilitate integrated energy system planning and modelling [196,197].…”

Section: Discussionmentioning

confidence: 99%

Modeling Energy Demand—A Systematic Literature Review

et al. 2021

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In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.

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Section: Discussionmentioning

confidence: 99%

Modeling Energy Demand—A Systematic Literature Review

et al. 2021

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“…2G 3G 4G 5G mostly used 1880-1930 1930-1980 1970-now currently used currently used With 4th Generation district heating networks (4GDH) smart thermal grids where introduced and efficiency increased by use of renewable energy sources [6]. In those smart thermal grids renewable energy can be shared within the whole district and the network itself can be used as heat storage [9]. Because of this the transition to 4GDH and implementation of smart thermal grids all over the world is inevitable.…”

Section: Gmentioning

confidence: 99%

Large-Scale Geothermal Collector Systems for 5th Generation District Heating and Cooling Networks

Zeh

Ohlsen²,

Philipp³

et al. 2021

Sustainability

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Low temperature district heating and cooling networks (5GDHC) in combination with very shallow geothermal energy potentials enable the complete renewable heating and cooling supply of settlements up to entire city districts. With the help of 5GDHC, heating and cooling can be distributed at a low temperature level with almost no distribution losses and made useable to consumers via decentralized heat pumps (HP). Numerous renewable heat sources, from wastewater heat exchangers and low-temperature industrial waste heat to borehole heat exchangers and large-scale geothermal collector systems (LSC), can be used for these networks. The use of large-scale geothermal collector systems also offers the opportunity to shift heating and cooling loads seasonally, contributing to flexibility in the heating network. In addition, the soil can be cooled below freezing point due to the strong regeneration caused by the solar irradiation. Multilayer geothermal collector systems can be used to deliberately generate excessive cooling of individual areas in order to provide cooling energy for residential buildings, office complexes or industrial applications. Planning these systems requires expertise and understanding regarding the interaction of these technologies in the overall system. This paper provides a summary of experience in planning 5GDHC with large-scale geothermal collector systems as well as other renewable heat sources.

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Deep Reinforcement Learning for Optimal Energy Management of Multi-energy Smart Grids

Bousnina

Guérassimoff

2022

Machine Learning, Optimization, and Data Science

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Smart Thermal Grids – A Review

Cited by 9 publications

References 4 publications

Modeling Energy Demand—A Systematic Literature Review

Modeling Energy Demand—A Systematic Literature Review

Large-Scale Geothermal Collector Systems for 5th Generation District Heating and Cooling Networks

Deep Reinforcement Learning for Optimal Energy Management of Multi-energy Smart Grids

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