Informatics Solution for Energy Efficiency Improvement and Consumption Management of Householders

Simona-Vasilica Oprea, Adela Bâra and Adriana Reveiu
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Simona-Vasilica Oprea: Department of Economic Informatics and Cybernetics, The Bucharest University of Economic Studies, Romana Square 6, Bucharest 010374, Romania
Adela Bâra: Department of Economic Informatics and Cybernetics, The Bucharest University of Economic Studies, Romana Square 6, Bucharest 010374, Romania
Adriana Reveiu: Department of Economic Informatics and Cybernetics, The Bucharest University of Economic Studies, Romana Square 6, Bucharest 010374, Romania

Energies, 2018, vol. 11, issue 1, 1-31

Abstract: Although in 2012 the European Union (EU) has promoted energy efficiency in order to ensure a gradual 20% reduction of energy consumption by 2020, its targets related to energy efficiency have increased and extended to new time horizons. Therefore, in 2016, a new proposal for 2030 of energy efficiency target of 30% has been agreed. However, during the last years, even if the electricity consumption by households decreased in the EU-28, the largest expansion was recorded in Romania. Taking into account that the projected consumption peak is increasing and energy consumption management for residential activities is an important measure for energy efficiency improvement since its ratio from total consumption can be around 25–30%, in this paper, we propose an informatics solution that assists both electricity suppliers/grid operators and consumers. It includes three models for electricity consumption optimization, profiles, clustering and forecast. By this solution, the daily operation of appliances can be optimized and scheduled to minimize the consumption peak and reduce the stress on the grid. For optimization purpose, we propose three algorithms for shifting the operation of the programmable appliances from peak to off-peak hours. This approach enables the supplier to apply attractive time-of-use tariffs due to the fact that by flattening the consumption peak, it becomes more predictable, and thus improves the strategies on the electricity markets. According to the results of the optimization process, we compare the proposed algorithms emphasizing the benefits. For building consumption profiles, we develop a clustering algorithm based on self-organizing maps. By running the algorithm for three scenarios, well-delimited profiles are obtained. As for the consumption forecast, highly accurate feedforward artificial neural networks algorithm with backpropagation is implemented. Finally, we test these algorithms using several datasets showing their performance and integrate them into a web-service informatics solution as a prototype.

Keywords: consumption management; programmable appliances; informatics solution; optimization algorithms; load profile; forecast; web-services; prototype (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 (7)

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