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Energy models for demand forecasting—A review

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  • Suganthi, L.
  • Samuel, Anand A.
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    Abstract

    Energy is vital for sustainable development of any nation – be it social, economic or environment. In the past decade energy consumption has increased exponentially globally. Energy management is crucial for the future economic prosperity and environmental security. Energy is linked to industrial production, agricultural output, health, access to water, population, education, quality of life, etc. Energy demand management is required for proper allocation of the available resources. During the last decade several new techniques are being used for energy demand management to accurately predict the future energy needs. In this paper an attempt is made to review the various energy demand forecasting models. Traditional methods such as time series, regression, econometric, ARIMA as well as soft computing techniques such as fuzzy logic, genetic algorithm, and neural networks are being extensively used for demand side management. Support vector regression, ant colony and particle swarm optimization are new techniques being adopted for energy demand forecasting. Bottom up models such as MARKAL and LEAP are also being used at the national and regional level for energy demand management.

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    Article provided by Elsevier in its journal Renewable and Sustainable Energy Reviews.

    Volume (Year): 16 (2012)
    Issue (Month): 2 ()
    Pages: 1223-1240

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    Handle: RePEc:eee:rensus:v:16:y:2012:i:2:p:1223-1240

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    Keywords: Energy models; Forecasting model; Energy demand management; Econometric models; Demand side management;

    References

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    Cited by:
    1. Jian Chai & Shubin Wang & Shouyang Wang & Ju’e Guo, 2012. "Demand Forecast of Petroleum Product Consumption in the Chinese Transportation Industry," Energies, MDPI, Open Access Journal, vol. 5(3), pages 577-598, March.
    2. Hocaoglu, Fatih Onur & Karanfil, Fatih, 2013. "A time series-based approach for renewable energy modeling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 204-214.
    3. Hossein Iranmanesh & Majid Abdollahzade & Arash Miranian, 2011. "Mid-Term Energy Demand Forecasting by Hybrid Neuro-Fuzzy Models," Energies, MDPI, Open Access Journal, vol. 5(1), pages 1-21, December.
    4. Aydin, Gokhan, 2014. "Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 382-389.
    5. Lin, Boqiang & Ouyang, Xiaoling, 2014. "Electricity demand and conservation potential in the Chinese nonmetallic mineral products industry," Energy Policy, Elsevier, vol. 68(C), pages 243-253.

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