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Title: | The effect of driver variables on the estimation of bivariate probability density of peak loads in long-term horizon |
Authors: | Kaheh, Z Shabanzadeh, M |
Keywords: | long-term forecasting;robust multi-energy systems;annual and seasonal peak load;self-organizing mixture network;probability density function;driver variables |
Issue Date: | 7-Jan-2021 |
Publisher: | Springer Nature |
Citation: | Kaheh, Z. and Shabanzadeh, M. (2021) 'The effect of driver variables on the estimation of bivariate probability density of peak loads in long-term horizon', Journal of Big Data, 8 (1), 15, pp. 1 - 17. doi: 10.1186/s40537-020-00404-8. |
Abstract: | It is evident that developing more accurate forecasting methods is the pillar of building robust multi-energy systems (MES). In this context, long-term forecasting is also indispensable to have a robust expansion planning program for modern power systems. While very short-term and short-term forecasting are usually represented with point estimation, this approach is highly unreliable in medium-term and long-term forecasting due to inherent uncertainty in predictors like weather variables in long terms. Accordingly, long-term forecasting is usually represented by probabilistic forecasting values which are based on probabilistic functions. In this paper, a self-organizing mixture network (SOMN) is developed to estimate the probability density function (PDF) of peak load in long-term horizons considering the most important drivers of seasonal similarity, population, gross domestic product (GDP), and electricity price. The proposed methodology is applied to forecast the PDF of annual and seasonal peak load in Queensland Australia. |
Description: | Availability of data and materials: The datasets analyzed during the current study are available from the corresponding author on request. |
URI: | https://bura.brunel.ac.uk/handle/2438/29693 |
DOI: | https://doi.org/10.1186/s40537-020-00404-8 |
Other Identifiers: | ORCiD: Zohreh Kaheh https://orcid.org/0000-0002-8518-8545 ORCiD: Morteza Shabanzadeh https://orcid.org/0000-0002-9989-1856 15 |
Appears in Collections: | Dept of Mathematics Research Papers |
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