Analyzing energy poverty using intelligent approach. Issue 8 (December 2020)

• Record Type: Journal Article
• Authors: Rajić, Milena N; Milovanović, Miroslav B; Antić, Dragan S; Maksimović, Rado M; Milosavljević, Pedja M; Pavlović, Dragan Lj
• Other Names: Caliskan Hakan guest-editor.; Reddy Koduru Janardhan guest-editor.; Sohret Yasin guest-editor.
• Is Part Of: Energy & environment. Volume 31:Issue 8(2020)
• Page Start: 1448
• Page End: 1472
• Publication Date: 2020-12
• Subjects: Energy poverty -- social factors -- energy management -- data mining -- neural network; Power resources -- Environmental aspects -- Periodicals; Power resources -- Periodicals; Renewable energy sources -- Periodicals; Energy policy -- Environmental aspects -- Periodicals; Ressources énergétiques; Politique énergétique; Energy policy -- Environmental aspects; Power resources; Power resources -- Environmental aspects; Renewable energy sources; Periodicals; 333.7
• DOI: 10.1177/0958305X20907087
• Languages: English
• ISSNs: 0958-305X

Abstract

The study represents a new approach of analyzing energy poverty using an intelligent method based on social factors. Energy poverty represents one of the most vital problems, especially in recent years, with serious implications in the social, economic, environmental, political, and health sphere. Identifying and measuring social energy parameters represents a challenge not only on the national, but also on the regional level. This research represents a new approach to neural network application in energy systems and energy resource planning activities with the aim of analyzing energy poverty with real socio-economic data. The analyzed data are associated with social factors within one country. The newly developed model includes a new data optimization framework for pre-processing and selecting the most important parameters from a raw dataset. In analyzing energy poverty, it is concluded that its weakness is based on the actual energy consumption of households (both electricity and heat consumption). The model presented in this paper uses 15 influential parameters in energy prediction.