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Decoupling Weather Influence from User Habits for an Optimal Electric Load Forecast System

Author

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  • Luca Massidda

    (CRS4, Center for Advanced Studies, Research and Development in Sardinia, loc. Piscina Manna ed. 1, 09010 Pula (CA), Italy)

  • Marino Marrocu

    (CRS4, Center for Advanced Studies, Research and Development in Sardinia, loc. Piscina Manna ed. 1, 09010 Pula (CA), Italy)

Abstract

The balance between production and consumption in a smart grid with high penetration of renewable sources and in the presence of energy storage systems benefits from an accurate load prediction. A general approach to load forecasting is not possible because of the additional complication due to the increasing presence of distributed and usually unmeasured photovoltaic production. Various methods are proposed in the literature that can be classified into two classes: those that predict by separating the portion of load due to consumption habits from the part of production due to local weather conditions, and those that attempt to predict the load as a whole. The characteristic that should lead to a preference for one approach over another is obviously the percentage of penetration of distributed production. The study site discussed in this document is the grid of Borkum, an island located in the North Sea. The advantages in terms of reducing forecasting errors for the electrical load, which can be obtained by using weather information, are explained. In particular, when comparing the results of different approaches gradually introducing weather forecasts, it is clear that the correct functional dependency of production has to be taken into account in order to obtain maximum yield from the available information. Where possible, this approach can significantly improve the quality of the forecasts, which in turn can improve the balance of a network—especially if energy storage systems are in place.

Suggested Citation

  • Luca Massidda & Marino Marrocu, 2017. "Decoupling Weather Influence from User Habits for an Optimal Electric Load Forecast System," Energies, MDPI, vol. 10(12), pages 1-16, December.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:2171-:d:123485
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    References listed on IDEAS

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    1. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
    2. Marino Marrocu & Luca Massidda, 2017. "A Simple and Effective Approach for the Prediction of Turbine Power Production From Wind Speed Forecast," Energies, MDPI, vol. 10(12), pages 1-14, November.
    3. Dongxiao Niu & Shuyu Dai, 2017. "A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Gre," Energies, MDPI, vol. 10(3), pages 1-20, March.
    4. Ghofrani, M. & Ghayekhloo, M. & Arabali, A. & Ghayekhloo, A., 2015. "A hybrid short-term load forecasting with a new input selection framework," Energy, Elsevier, vol. 81(C), pages 777-786.
    5. Kou, Peng & Gao, Feng & Guan, Xiaohong, 2015. "Stochastic predictive control of battery energy storage for wind farm dispatching: Using probabilistic wind power forecasts," Renewable Energy, Elsevier, vol. 80(C), pages 286-300.
    6. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
    7. Sepasi, Saeed & Reihani, Ehsan & Howlader, Abdul M. & Roose, Leon R. & Matsuura, Marc M., 2017. "Very short term load forecasting of a distribution system with high PV penetration," Renewable Energy, Elsevier, vol. 106(C), pages 142-148.
    8. Kaur, Amanpreet & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Net load forecasting for high renewable energy penetration grids," Energy, Elsevier, vol. 114(C), pages 1073-1084.
    9. Li, Song & Goel, Lalit & Wang, Peng, 2016. "An ensemble approach for short-term load forecasting by extreme learning machine," Applied Energy, Elsevier, vol. 170(C), pages 22-29.
    10. Marčiukaitis, Mantas & Žutautaitė, Inga & Martišauskas, Linas & Jokšas, Benas & Gecevičius, Giedrius & Sfetsos, Athanasios, 2017. "Non-linear regression model for wind turbine power curve," Renewable Energy, Elsevier, vol. 113(C), pages 732-741.
    11. Men, Zhongxian & Yee, Eugene & Lien, Fue-Sang & Wen, Deyong & Chen, Yongsheng, 2016. "Short-term wind speed and power forecasting using an ensemble of mixture density neural networks," Renewable Energy, Elsevier, vol. 87(P1), pages 203-211.
    12. Zhao, Haoran & Wu, Qiuwei & Hu, Shuju & Xu, Honghua & Rasmussen, Claus Nygaard, 2015. "Review of energy storage system for wind power integration support," Applied Energy, Elsevier, vol. 137(C), pages 545-553.
    13. Yuan, Shengxi & Kocaman, Ayse Selin & Modi, Vijay, 2017. "Benefits of forecasting and energy storage in isolated grids with large wind penetration – The case of Sao Vicente," Renewable Energy, Elsevier, vol. 105(C), pages 167-174.
    14. Li, Yong & Wen, Zhe & Cao, Yijia & Tan, Yi & Sidorov, Denis & Panasetsky, Daniil, 2017. "A combined forecasting approach with model self-adjustment for renewable generations and energy loads in smart community," Energy, Elsevier, vol. 129(C), pages 216-227.
    15. Ruth, Matthias & Lin, Ai-Chen, 2006. "Regional energy demand and adaptations to climate change: Methodology and application to the state of Maryland, USA," Energy Policy, Elsevier, vol. 34(17), pages 2820-2833, November.
    16. Vaz, A.G.R. & Elsinga, B. & van Sark, W.G.J.H.M. & Brito, M.C., 2016. "An artificial neural network to assess the impact of neighbouring photovoltaic systems in power forecasting in Utrecht, the Netherlands," Renewable Energy, Elsevier, vol. 85(C), pages 631-641.
    17. J W Taylor, 2003. "Short-term electricity demand forecasting using double seasonal exponential smoothing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 799-805, August.
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    Cited by:

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    7. Massidda, Luca & Marrocu, Marino, 2023. "Total and thermal load forecasting in residential communities through probabilistic methods and causal machine learning," Applied Energy, Elsevier, vol. 351(C).
    8. Hyojoo Son & Changwan Kim, 2020. "A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity," Sustainability, MDPI, vol. 12(8), pages 1-16, April.

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