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Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting

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  • Ghadimi, Noradin
  • Akbarimajd, Adel
  • Shayeghi, Hossein
  • Abedinia, Oveis

Abstract

Short-term load forecasting is of major interest for the restructured environment of the electricity market. Accurate load forecasting is essential for effective power system operation, but electricity load is non-linear with a high level of volatility. Predicting such complex signals requires suitable prediction tools. This paper proposes a hybrid forecast strategy including novel feature selection technique, and a complex forecast engine based on a new intelligent algorithm. The electricity load signal is first filtered by feature selection technique to select appropriate candidates as input for the forecast engine. Then, the proposed two stage forecast engine is implemented based on ridgelet and Elman neural networks. All forecast engine parameters are chosen based on a novel intelligent algorithm to improve its accuracy and capability. Different electricity markets were considered as test cases to compare the proposed method with several current algorithms. Additionally, the proposed forecasting model measures the absolute forecasting errors in this work (among seven types of measurements i.e., absolute forecasting errors, measures based on percentage errors, symmetric errors, measures based on relative errors, scaled errors, relative measures and other error measures). The results validate the effectiveness of the proposed method.

Suggested Citation

  • Ghadimi, Noradin & Akbarimajd, Adel & Shayeghi, Hossein & Abedinia, Oveis, 2018. "Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting," Energy, Elsevier, vol. 161(C), pages 130-142.
    • Handle: RePEc:eee:energy:v:161:y:2018:i:c:p:130-142
    DOI: 10.1016/j.energy.2018.07.088
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    References listed on IDEAS

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    1. Liu, Yang & Wang, Wei & Ghadimi, Noradin, 2017. "Electricity load forecasting by an improved forecast engine for building level consumers," Energy, Elsevier, vol. 139(C), pages 18-30.
    2. Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.
    3. Deihimi, Ali & Orang, Omid & Showkati, Hemen, 2013. "Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction," Energy, Elsevier, vol. 57(C), pages 382-401.
    4. Poghosyan, Anush & Greetham, Danica Vukadinović & Haben, Stephen & Lee, Tamsin, 2015. "Long term individual load forecast under different electrical vehicles uptake scenarios," Applied Energy, Elsevier, vol. 157(C), pages 699-709.
    5. Deihimi, Ali & Showkati, Hemen, 2012. "Application of echo state networks in short-term electric load forecasting," Energy, Elsevier, vol. 39(1), pages 327-340.
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