Smart Buildings for Decarbonised Energy Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (31 July 2019) | Viewed by 2538

Special Issue Editor


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Guest Editor
Institute of Energy and Sustainable Development (IESD), School of Engineering and Sustainable Development, De Montfort University, Leicester LE1 9BH, UK
Interests: energy in buildings; climate change and buildings; modelling; monitoring buildings; building retrofit
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Special Issue Information

Dear Colleagues,

Climate change, poor air quality, resource depletion, and energy security concerns are driving a revolution in the world’s energy systems. Buildings account for about one third of final energy use globally, and almost a fifth of energy-related greenhouse gas emissions. Decarbonisation of heating includes a shift from gas, to electric heat pumps or low carbon district heating. In parallel, there is a shift from fossil fuels for road transport to electric battery vehicles. Buildings are evolving from passive energy consumers to active participants of the energy system, with on-site low carbon generation, storage, charging of vehicles, and the ability to modify their demand on the grid. Advances in information technology provide opportunities for data capture and analysis of buildings and energy systems, facilitating 'smart' approaches to their management. These changes create multiple challenges across many disciplines for the design of buildings, their services, and energy system providers. The aim of this Special Issue is to bring together up-to-date research on the interaction of buildings with modern, decarbonising energy systems. This includes, but is not limited to:

  • Effective management of low carbon district heating
  • Electricity demand shaping by buildings on smart grids
  • Generation and storage of heat and electricity on site to reduce peak demands
  • Impact and management of vehicle charging on domestic supplies
  • Use of heat pumps and grid interactions
  • Management of smart grids in relation to buildings
  • Integration of community renewable energy for local consumption and with the grid

Dr. Andrew Wright
Guest Editor

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Published Papers (1 paper)

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Research

17 pages, 762 KiB  
Article
Using Different ML Algorithms and Hyperparameter Optimization to Predict Heat Meters’ Failures
by Przemysław Pałasz and Radosław Przysowa
Appl. Sci. 2019, 9(18), 3719; https://doi.org/10.3390/app9183719 - 06 Sep 2019
Cited by 6 | Viewed by 2275
Abstract
The need to increase the energy efficiency of buildings, as well as the use of local renewable heat sources has caused heat meters to be used not only to calculate the consumed energy, but also for the active management of central heating systems. [...] Read more.
The need to increase the energy efficiency of buildings, as well as the use of local renewable heat sources has caused heat meters to be used not only to calculate the consumed energy, but also for the active management of central heating systems. Increasing the reading frequency and the use of measurement data to control the heating system expands the requirements for the reliability of heat meters. The aim of the research is to analyze a large set of meters in the real network and predict their faults to avoid inaccurate readings, incorrect billing, heating system disruption, and unnecessary maintenance. The reliability analysis of heat meters, based on historical data collected over several years, shows some regularities, which cannot be easily described by physics-based models. The failure rate is almost constant and does depend on the past, but is a non-linear combination of state variables. To predict meters’ failures in the next billing period, three independent machine learning models are implemented and compared with selected metrics, because even the high performance of a single model (87% true positive for neural network) may be insufficient to make a maintenance decision. Additionally, performing hyperparameter optimization boosts the models’ performance by a few percent. Finally, three improved models are used to build an ensemble classifier, which outperforms the individual models. The proposed procedure ensures the high efficiency of fault detection (>95%), while maintaining overfitting at the minimum level. The methodology is universal and can be utilized to study the reliability and predict faults of other types of meters and different objects with the constant failure rate. Full article
(This article belongs to the Special Issue Smart Buildings for Decarbonised Energy Systems)
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