Social and Infrastructural Conditioning of Lowering Energy Costs and Improving the Energy Efficiency of Buildings in the Context of the Local Energy Policy

Maria Mrówczyńska, Marta Skiba, Anna Bazan-Krzywoszańska, Dorota Bazuń and Mariusz Kwiatkowski
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Maria Mrówczyńska: Architecture and Environmental Engineering, Faculty of Civil Engineering, University of Zielona Góra, 65-417 Zielona Góra, Licealna 9, Poland
Marta Skiba: Architecture and Environmental Engineering, Faculty of Civil Engineering, University of Zielona Góra, 65-417 Zielona Góra, Licealna 9, Poland
Anna Bazan-Krzywoszańska: Architecture and Environmental Engineering, Faculty of Civil Engineering, University of Zielona Góra, 65-417 Zielona Góra, Licealna 9, Poland
Dorota Bazuń: Psychology and Sociology, Faculty of Education, University of Zielona Góra, 65-417 Zielona Góra, Licealna 9, Poland
Mariusz Kwiatkowski: Psychology and Sociology, Faculty of Education, University of Zielona Góra, 65-417 Zielona Góra, Licealna 9, Poland

Energies, 2018, vol. 11, issue 9, 1-16

Abstract: The main problem in creating successful efficiency improvement policies is adjusting objectives to local development programs, dependent on public awareness. This article attempts to find a framework for the costs of changing energy policies using neural networks to identify the social-infrastructure conditions. An analysis model is presented of social-infrastructure conditions of energy costs reduction and buildings’ efficiency improvement. Data were obtained from standardized interviews with Zielona Góra, Poland inhabitants and the Town Energy Audit documentation. The data were analyzed using an artificial neural network. This allowed the creation of a model to estimate the cost inhabitants will incur if the energy is sourced from RES (Renewable Energy Systems). The city social-infrastructural correlation model enabled diagnosing its fragments that can support decision-making. The paper contributes to the current knowledge demonstrating the possibilities of hierarchical investments, different for various buildings and neighborhoods, that allow for rational public funding. Knowledge of the correlation conditions matters when implementing effective local policy. This work is based on pilot studies not financed by the parties concerned. Multiple themes were intentionally investigated: emission control, reducing energy consumption, renovating buildings, supplying with RES, and energy poverty, to show methods to match the goal (hard) to social conditions (soft), rarely presented in studies.

Keywords: local energy policy; energy efficiency of buildings; neural network; social-infrastructural correlation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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