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Data Analysis On Non-Residential Electricity Consumption By Statistical And Mathematical Techniques In View Of Devising Appropriate Consumption Strategies

Author

Listed:
  • George Căruțașu

    (Romanian-American University, Bucharest, Romania)

  • Alexandru Pîrjan

    (Romanian-American University, Bucharest, Romania)

  • Cristina Coculescu

    (Romanian-American University, Bucharest, Romania)

  • Justina Lavinia Stănică

    (Romanian-American University, Bucharest, Romania)

  • Mironela Pîrnău

    (Titu Maiorescu University, Bucharest, Romania, Romanian-American University, Bucharest, Romania)

Abstract

The aim of this paper is to analyze, process and interpret, from economic and statistical perspectives, the data regarding the quantity of electric energy, measured at the non-residential consumers’ level. Our intention was to track and analyze the electric energy consumption level at hourly intervals for a real consumer in Romania. The measurements were carried out in MWh, and collected in databases, in order to facilitate the application of the calculation methods. The results and their interpretations facilitate the scientific substantiation of new policies in order to optimize the electric energy consumption. The statistical and mathematical methods employed represent viable tools in achieving an adequate data analysis on non-residential electricity consumption in view of devising appropriate consumption strategies. These will be transmitted and proposed to the real consumer as scenarios of its analyzed consumption profile. After having experimented several methods for approximating the data repartition, we have concluded that by adjusting the primary data with the estimated normal repartition one obtains the ideal model in the case of hourly electricity consumption of non-residential consumers offering valuable insights regarding the modelling of their consumption patterns.

Suggested Citation

  • George Căruțașu & Alexandru Pîrjan & Cristina Coculescu & Justina Lavinia Stănică & Mironela Pîrnău, 2019. "Data Analysis On Non-Residential Electricity Consumption By Statistical And Mathematical Techniques In View Of Devising Appropriate Consumption Strategies," Journal of Information Systems & Operations Management, Romanian-American University, vol. 13(2), pages 27-48, December.
    • Handle: RePEc:rau:jisomg:v:13:y:2019:i:2:p:27-48
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    References listed on IDEAS

    as
    1. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    2. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    3. Mat Daut, Mohammad Azhar & Hassan, Mohammad Yusri & Abdullah, Hayati & Rahman, Hasimah Abdul & Abdullah, Md Pauzi & Hussin, Faridah, 2017. "Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1108-1118.
    4. Justina Lavinia St?nic? & George Carutasu & Alexandru Pîrjan & Cristina Coculescu, 2018. "Iot Cloud Solution For Efficient Electricity Consumption," Journal of Information Systems & Operations Management, Romanian-American University, vol. 12(1), pages 45-57, May.
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