Reforming Home Energy Consumption Behavior based on Mining Techniques, A Collaborative Home Appliances Approach
Keywords:Energy Consumption, Associations Rules Mining, Collaborative Approach, Home Appliances, Apriori Algorithm
AbstractEnergy Consumption has become one of the strategic objectives all over the world which is not only the enterprises obligation but it should also be all citizens’ obligation. Focusing on individuals’ energy consumption, a vital approach for saving energy is a collaboration strategy which aims at sharing the home appliances for best usage. In this research, an approach is proposed for recommending the collaboration plan for adjacent houses in different perspectives, they are sharing appliances and minimizing the consumption rate. The research adopts the mining techniques in order to explore the required associations targeting to build the road map for appliances consumption. Representing the proposed approach is performed through a formal representation to the contributed stakeholders and their relations, representing the relations and the required associations in a semantic network represents ion, and each stage is discussed in details. The research applied the experiment on two of the benchmark datasets which references are mentioned, all stages are applied and associations are explored with confidence above 90% and the results confirmed the applicability of the proposed approach
Abdi, F., & Abolmakarem, S. (2018). Customer Behavior Mining Framework (CBMF) using clustering and classification techniques. Journal of Industrial Engineering International.
Ali, U., Buccella, C., & Cecati, C. (2016). Households Electricity Consumption Analysis with Data Mining Techniques. IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society. IEEE.
Chung, M., Yang, Y., Lee, K., Lee, J., & Moon, J. (2017). Application of artificial neural networks for determining energy efficient operating set-points of the VRF cooling system. Building and Environment, 125, 77-87.
Dogan, O., Martinez-Millana, A., Rojas, E., Sepúlveda, M., Munoz-Gama, J., Traver, V., & Fernandez-Llatas, C. (2019). Individual Behavior Modeling with Sensors Using Process Mining. Electronics, 1-17.
Elmasry, H. E., Khedr, A. E., & Nasr, M. M. (2019). An adaptive technique for cost reduction in cloud data centre environment. International journal of Grid and Utility Computing, 10(5), 448-464.
ewood. (2016). Home Energy Consumption. Diambil kembali dari data.world.
Gabaldón, A., Álvarez, C., del Carmen Ruiz-Abellón, M., Guillamón, A., Valero-Verdú, S., Molina, R., & García-Garre, A. (2018). Integration of Methodologies for the Evaluation of Offer Curves in Energy and Capacity Markets through Energy Efficiency and Demand Response. MDPI.
Helmy, Y., Khedr, A. E., Kolief, S., & Haggag, E. (2019). An Enhanced Business Intelligence Approach for Increasing Customer Satisfaction Using Mining Techniques. International Journal of Computer Science and Information Security (IJCSIS), 17(4).
Idrees, M. A., ElSeddawy, A. I., & Zeidan, M. O. (2019). Knowledge Discovery based Framework for Enhancing the House of Quality. International Journal of Advanced Computer Science and Applications (IJACSA), 10(7), 324-331.
Jeswiet, J., Archibald, J., Thorley, U., & De Souza, E. (2015). Energy Use in Premanufacture (Mining). The 22nd CIRP conference on Life Cycle Engineering (hal. 816-821). Elsevier.
Kelly, J., & Knottenbelt, W. (2015). The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Scientific Data, 2(15).
Khedr, A. E., & Idrees, A. M. (2017). Adapting Load Balancing Techniques for Improving the Performance of e-Learning Educational Process. Journal of Computers, 12(3), 250-257.
Khedr, A. E., & Idrees, A. M. (2017). Enhanced e-Learning System for e-Courses Based on Cloud Computing. Journal of Computers, 12(1).
Konstantinos, B., & Georgios, S. (2019). EFFICIENT POWER THEFT DETECTION FOR RESIDENTIAL CONSUMERS USING MEAN SHIFT. International Journal of Artificial Intelligence and Applications (IJAIA), 10(1), 69-85.
Mostafa, A., Khedr, A. E., & Abdo, A. (2017). Advising Approach to Enhance Students' Performance Level in Higher Education Environments. Journal of Computer Science, 13(5), 130-139.
Shaker, S., Tamer, M., & Khedr, A. E. (2019). A Proposed Framework for Reducing Electricity Consumption in Smart Homes using Big Data Analytics. Journal of Computer Science, 15(4), 537-549.
Singh, S., & Yassine, A. (2017). Mining Energy Consumption Behavior Patterns for Households in Smart Grid. IEEE Transactions on Emerging Topics in Computing, 5.
Sultan, N., Khedr, A. E., Idrees, A. M., & Kholeif, S. (2017). Data Mining Approach for Detecting Key Performance Indicators. Journal of Artificial Intelligence, 10(2), 59-65.
Victor, V., Thoppan, J., Nathan, R., & Maria, F. (2018). Factors Influencing Consumer Behavior and Prospective Purchase Decisions in a Dynamic Pricing Environment—An Exploratory Factor Analysis Approach. Social Sciences, 7(153), 1-24