Parametric Time-series Modelling of London Smart Meter Data for Short-term Demand Forecasting-Reference-Cited by-同舟云学术

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Parametric Time-series Modelling of London Smart Meter Data for Short-term Demand Forecasting

Published:2023-08-09 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 3rd International Conference on Sustainable Energy and Future Electric Transportation (SEFET)
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Author:

Mondal Arijit¹,Das Saptarshi²

Affiliation:

1. University of Exeter,Faculty of Environment, Science and Economy,Department of Computer Science,Devon,United Kingdom,EX4 4QE

2. University of Exeter,Centre for Environmental Mathematics, Faculty of Environment, Science and Economy,Cornwall,United Kingdom,TR10 9FE

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10244770/10244772/10245994.pdf?arnumber=10245994

Reference31 articles.

1. Electricity consumption forecasting in Brazil: A spatial econometrics approach

2. Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models

3. Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model

4. Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model

5. Scikit-learn: Machine learning in Python;pedregosa;Journal of Machine Learning Research,2011

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