Probability estimation of the city's energy efficiency improvement as a result of using the phase change materials in heating networks
Marta Skiba , Maria Mrówczyńska , M. Sztubecka , A. Bazan-Krzywoszańska , Jan Kazak , Agnieszka Leśniak , Filip Janowiec
AbstractOne of the problems in cities is improving energy efficiency. Energy consumption and the efficient use of district heating networks affect the environment, society, and economy. Modern materials to increase buildings' energy efficiency become necessary. The use of phase change materials (PCM) is a current issue and many researchers' interest. PCM reduces energy consumption in buildings due to their ability to absorb and release energy. PCM use seems to be highly justified as regards energy efficiency policy-making considering the city's investment scale. Therefore, research was undertaken on the possibility of reducing energy consumption in the city by using PCM in municipal heating networks, as those for which planning can be carried out systemically. The research's original element is integrating geographic systems with artificial intelligence and statistical methods to estimation the probability of improving buildings' energy efficiency in urban areas based on an identified set of criteria of an interdisciplinary type. The proposed innovative approach was used to analyze the medium-sized city located in Eastern Europe. The results showed that buildings could be classified according to the probability of energy improvement at the limit of 30%. Suggestions were made for adapting the proposed method to more general cases.
|Journal series||Energy, ISSN 0360-5442, e-ISSN 1873-6785, (N/A 200 pkt)|
|Publication size in sheets||0.7|
|Keywords in English||Energy efficiency improvements; Energy policy-making; Urban analysis; Phase change materials; Multi-criteria analysis; Bayesian network|
|License||Journal (articles only); published final; ; with publication|
|Score||= 200.0, 20-04-2021, ArticleFromJournal|
|Publication indicators||= 0; : 2018 = 1.822; : 2019 = 6.082 (2) - 2019=6.046 (5)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.