Short-term load forecast using ensemble neuro-fuzzy model

In this paper, Takagi-Sugeno-Kang neuro-fuzzy model is trained using locally linear model tree (LOLIMOT) method to forecast day-ahead hourly load profile. The proposed approach is applied to a real load profile measured in Iran as a geographically spread case study. The effects of partitioning the p...

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Bibliographic Details
Published in:	Energy (Oxford) Vol. 196; p. 117127
Main Authors:	Malekizadeh, M., Karami, H., Karimi, M., Moshari, A., Sanjari, M.J.
Format:	Journal Article
Language:	English
Published:	Oxford Elsevier Ltd 01-04-2020 Elsevier BV
Subjects:	Artificial neural networks Electricity consumption Flexible network topology Forecasting Fuzzy logic LOLIMOT training algorithm Mathematical models Network topologies Neuro-fuzzy model Short-term load forecasting Takagi-Sugeno-Kang model Topology Neuro-fuzzy model Takagi-Sugeno-Kang model Short-term load forecasting Flexible network topology LOLIMOT training algorithm
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Summary:	In this paper, Takagi-Sugeno-Kang neuro-fuzzy model is trained using locally linear model tree (LOLIMOT) method to forecast day-ahead hourly load profile. The proposed approach is applied to a real load profile measured in Iran as a geographically spread case study. The effects of partitioning the power system to smaller regions on the load forecasting and its advantages, such as practical consideration of daily average temperature data, are also shown. Moreover, a set of preprocessing approaches is proposed and implemented on historical load data to improve forecasting results. It is shown that by using LOLIMOT, the neuro-fuzzy model does not need the predetermined settings, such as the number of neurons, membership functions or fuzzy rules by an expert because all the parameters are set by the LOLIMOT method. This approach leads to the flexible network topology of the trained model for different days, which leads to extract the load profile trends more effectively. [Display omitted] •Forecasting day-ahead hourly load profile of Iran as geographically spread case study.•Using Takagi-Sugeno-Kang neuro-fuzzy model trained by locally linear model tree.•Needless of parameters predetermination by the proposed approach.•Partitioning the power system for effective practical consideration of temperature.•Proposing some preprocessing approaches on historical data to improve prediction.
ISSN:	0360-5442 1873-6785
DOI:	10.1016/j.energy.2020.117127