 Short-term load forecast using ensemble neuro-fuzzy modelM. Malekizadeh, H. Karami, M. Karimi, A. Moshari and M.J. Sanjari Energy, 2020, vol. 196, issue C Abstract: 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. Keywords: Short-term load forecasting; Neuro-fuzzy model; LOLIMOT training algorithm; Takagi-Sugeno-Kang model; Flexible network topology (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:196:y:2020:i:c:s0360544220302346 DOI: 10.1016/j.energy.2020.117127 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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