Implementation of Load Demand Prediction Model for a Domestic Load Center Using Different Machine Learning Algorithms—A Comparison-Reference-Cited by-同舟云学术

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Implementation of Load Demand Prediction Model for a Domestic Load Center Using Different Machine Learning Algorithms—A Comparison

Published:2022 Issue: Volume: Page:445-467
ISSN:2367-3370
Container-title:Pervasive Computing and Social Networking
language:
Short-container-title:

Author:

Pratapa Raju M.,Jaya Laxmi A.

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-5640-8_35

Reference69 articles.

1. Taylor JW (2010) Triple seasonal methods for short-term electricity demand forecasting. Eur J Oper Res 204(1):139–152

2. Taylor JW, De Menezes LM, McSharry PE (2006) A comparison of univariate methods for forecasting electricity demand up to a day ahead. Int J Forecast 22(1):1–16

3. Taylor JW, Buizza R (2003) Using weather ensemble predictions in electricity demand forecasting using weather ensemble predictions in electricity demand forecasting. Int J Forecast 19(1):57–70

4. Gould PG, Koehler AB, Ord JK, Snyder RD, Hyndman RJ, Vahid-Araghi F (2008) Forecasting time series with multiple seasonal patterns. Euro J Oper Res 191(1):207–222

5. Al-Hamadi HM, Soliman SA (2008) Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model. Electr Power Syst Res 68(1):47–59

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