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Advances in Energy and Built Environment pp 111–121Cite as

Electricity Demand Forecasting Using Regression Techniques

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Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 36))

Abstract

Accurate demand forecasting is very important for electric utilities in a competitive environment created by the electric industry deregulation. By using regression analysis, we have analyzed the electricity demand forecast of all-India demand data. Forecast is compared with partial end-use technique. Multiple regression method has been used for forecasting electricity demand by selecting various combinations of independent variables such as Net State Domestic Product (NSDP), Sector-wise Domestic Savings Household sector, Consumers, Connected Load, etc. It was found that sector-wise Net Domestic Savings Household sector was very effective for ascertaining the future electricity demand in the domestic sector in the country.

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Author information

Authors and Affiliations

  1. Department of Physics, Noida International University, Greater Noida, Uttar Pradesh, India

    Tanveer Ahmad Wani

  2. Department of Mechanical Engineering, Noida International University, Greater Noida, Uttar Pradesh, India

    Mohd Shiraz

Authors
  1. Tanveer Ahmad Wani
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  2. Mohd Shiraz
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Corresponding author

Correspondence to Tanveer Ahmad Wani .

Editor information

Editors and Affiliations

  1. Hunan University, Changsha, China

    Guoqiang Zhang

  2. Siksha ‘O’ Anusandhan University, Bhubaneswar, India

    N. D. Kaushika

  3. Indian Institute of Technology Delhi, New Delhi, India

    S. C. Kaushik

  4. Amity University, Noida, India

    R. K. Tomar

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Wani, T.A., Shiraz, M. (2020). Electricity Demand Forecasting Using Regression Techniques. In: Zhang, G., Kaushika, N., Kaushik, S., Tomar, R. (eds) Advances in Energy and Built Environment. Lecture Notes in Civil Engineering , vol 36. Springer, Singapore. https://doi.org/10.1007/978-981-13-7557-6_9

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  • DOI: https://doi.org/10.1007/978-981-13-7557-6_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7556-9

  • Online ISBN: 978-981-13-7557-6

  • eBook Packages: EngineeringEngineering (R0)

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