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Informatics Solution for Energy Efficiency Improvement and Consumption Management of Householders

Author

Listed:
  • 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)

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.

Suggested Citation

  • Simona-Vasilica Oprea & Adela Bâra & Adriana Reveiu, 2018. "Informatics Solution for Energy Efficiency Improvement and Consumption Management of Householders," Energies, MDPI, vol. 11(1), pages 1-31, January.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:138-:d:125671
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    References listed on IDEAS

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    2. Omaji Samuel & Sakeena Javaid & Nadeem Javaid & Syed Hassan Ahmed & Muhammad Khalil Afzal & Farruh Ishmanov, 2018. "An Efficient Power Scheduling in Smart Homes Using Jaya Based Optimization with Time-of-Use and Critical Peak Pricing Schemes," Energies, MDPI, vol. 11(11), pages 1-27, November.
    3. Anh-Duc Nguyen & Van-Hai Bui & Akhtar Hussain & Duc-Huy Nguyen & Hak-Man Kim, 2018. "Impact of Demand Response Programs on Optimal Operation of Multi-Microgrid System," Energies, MDPI, vol. 11(6), pages 1-18, June.
    4. Miltiadis Alamaniotis & Nikolaos Gatsis, 2019. "Evolutionary Multi-Objective Cost and Privacy Driven Load Morphing in Smart Electricity Grid Partition," Energies, MDPI, vol. 12(13), pages 1-18, June.
    5. Nakyoung Kim & Sangdon Park & Joohyung Lee & Jun Kyun Choi, 2018. "Load Profile Extraction by Mean-Shift Clustering with Sample Pearson Correlation Coefficient Distance," Energies, MDPI, vol. 11(9), pages 1-20, September.
    6. Fei Wang & Liming Liu & Yili Yu & Gang Li & Jessica Li & Miadreza Shafie-khah & João P. S. Catalão, 2018. "Impact Analysis of Customized Feedback Interventions on Residential Electricity Load Consumption Behavior for Demand Response," Energies, MDPI, vol. 11(4), pages 1-22, March.
    7. Fei Wang & Yili Yu & Xinkang Wang & Hui Ren & Miadreza Shafie-Khah & João P. S. Catalão, 2018. "Residential Electricity Consumption Level Impact Factor Analysis Based on Wrapper Feature Selection and Multinomial Logistic Regression," Energies, MDPI, vol. 11(5), pages 1-26, May.

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