A self-partitioning local neuro fuzzy model for short-term load forecasting in smart grids

Z. Tavassoli-Hojati, S.F. Ghaderi, H. Iranmanesh, P. Hilber and E. Shayesteh

Energy, 2020, vol. 199, issue C

Abstract: Electric power systems are moving toward smarter and more sustainable systems. These trends result in several positive advantages such as active participation of customers in electricity markets. However, resulting demand side flexibilities cause high demand fluctuations and increase the difficulty to maintain the power balance and reliability of smart grids. To address this challenge, this paper proposes a self-partitioning local neuro fuzzy model, which is capable of performing a fast and accurate short-term load forecasting. The proposed model, not only maintains the linearity as well as learning–from-data property via their fuzzy inference systems of local linear neuro fuzzy, but also benefits from partitioning the input space into linear and nonlinear vectors and assigning them separately into different local models. The proposed model is trained with the hierarchical binary-tree learning algorithm and rule premises are calculated through sigmoid partitioning functions. These appealing properties make the model appropriate for a fast and accurate analysis of the load time series featuring both linear and nonlinear characteristics. The effectiveness of the proposed model is compared with recently published forecasting models in terms of statistical performance.

Keywords: Short-term load forecasting; Smart grids; Self-partitioning local neuro fuzzy model; Hierarchical binary tree learning (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544220306216
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:199:y:2020:i:c:s0360544220306216

DOI: 10.1016/j.energy.2020.117514

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().