Identification of fuzzy model for short-term load forecasting using evolutionary programming and orthogonal least squares

Ye, B.; Yan, Nian; Guo, Chuangxin; Cao, Yi

doi:10.1109/pes.2006.1709372

Cited by 9 publications

(2 citation statements)

References 18 publications

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“…The short-term forecasting can be also supported by various regression models (Liu, Huang, & Hsien, 2005;Zhang, & Li, 2011). Moreover, different methods like neural networks, multiple classifier systems, evolutionary programming, orthogonal lest squares and genetic algorithms are used to create the more accurate short-term energy forecasts (Ye et al, 2006;Chan et al, 2011). There are also approaches based on autoregressive methods (ARMA, ARIMA, EGARCH) and exponential smoothing (Contreras et al, 2003;Bowden, & Payne, 2008).…”

Section: Related Workmentioning

confidence: 99%

Profiling of Prosumers for the Needs of Electric Energy Demand Estimation in Microgrids

Fabisz

Filipowska

Hossa

2015

International Journal of Energy Optimization and Engineering

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Nowadays, a lot of attention regarding smart grids' development is devoted to delivery of methods for estimation of the energy demand taking into account the behavior of network participants (being single prosumers or groups of prosumers). These methods take an advantage from an analysis of the ex-post data on energy consumption, usually with no additional data about profiles of prosumers. The goal of this paper is to present and validate a method for an energy demand forecasting based on profiling of prosumers that enables estimation of the energy demand for every user stereotype, every hour, every day of the year and even for every device. The paper presents possible scenarios on how the proposed approach can be used for the benefit of the microgrid.

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Section: Related Workmentioning

confidence: 99%

Profiling of Prosumers for the Needs of Electric Energy Demand Estimation in Microgrids

Fabisz

Filipowska

Hossa

2015

International Journal of Energy Optimization and Engineering

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“…Multilayer models based upon artificial neural networks [28] are presented in ( [3], [29]) for load forecasting. Alternative models that are not classified in either of the above mentioned categories are fuzzy methods, used in [30] or [31].…”

Section: Introductionmentioning

confidence: 99%

Data Selection for Short Term load forecasting

Pereira,

Herrera,

Gómez-Verdejo

et al. 2019

Preprint

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Power load forecast with Machine Learning is a fairly mature application of artificial intelligence and it is indispensable in operation, control and planning. Data selection techniqies have been hardly used in this application. However, the use of such techniques could be beneficial provided the assumption that the data is identically distributed is clearly not true in load forecasting, but it is cyclostationary. In this work we present a fully automatic methodology to determine what are the most adequate data to train a predictor which is based on a full Bayesian probabilistic model. We assess the performance of the method with experiments based on real publicly available data recorded from several years in the United States of America.

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Wind speed forecasting based on model selection, fuzzy cluster, and multi-objective algorithm and wind energy simulation by Betz's theory

Zhang¹,

Wang²,

Liao³

et al. 2022

Expert Systems with Applications

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Identification of fuzzy model for short-term load forecasting using evolutionary programming and orthogonal least squares

Cited by 9 publications

References 18 publications

Profiling of Prosumers for the Needs of Electric Energy Demand Estimation in Microgrids

Profiling of Prosumers for the Needs of Electric Energy Demand Estimation in Microgrids

Data Selection for Short Term load forecasting

Wind speed forecasting based on model selection, fuzzy cluster, and multi-objective algorithm and wind energy simulation by Betz's theory

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