A data-driven knowledge-based system with reasoning under uncertain evidence for regional long-term hourly load forecasting

M. Rostam Niakan Kalhori, I. Taheri Emami, F. Fallahi and M. Tabarzadi

Applied Energy, 2022, vol. 314, issue C, No S0306261922003853

Abstract: Reliable long-term hourly load forecasting is imperative for electricity utilities and planners' decision-making, especially in generation-transmission expansion planning. This paper develops a data-driven knowledge-based system for regional hourly load forecasting in the long-term horizon with contributions in both knowledge base and inference engine. In this regard, in the knowledge base, two types of driving factors are taken into account to determine the forecasting model: long-term trend-related features such as macro-economic factors and short-term factors such as temperature. The knowledge base is decomposed into three-step-related parts to deal with these groups' substantial differences in frequency. Firstly, the long-term trends are identified by the tendency variables. Then, the short-term variability is considered by temperature-related variables in addition to day type. Machine learning regression methods, support vector machines, random forest, and artificial neural networks are compared to determine the non-linear relationship of variables in these steps. The results of these steps are combined in the third step. Then, in the inference engine, a new reasoning method based on fuzzy logic is represented to forecast the regional long-term hourly load under uncertain evidence of temperature as an effective non-predictable feature in the long run. To evaluate the performance of the proposed system, a comparison against five systems is conducted. The results show the superiority of this system compared to the other systems for a publicly available dataset (ISO New England electricity market) as well as Iran’s residential, commercial and agricultural electricity load.

Keywords: Knowledge-based system; Machine learning; Reasoning under uncertainty; Long-term hourly load forecasting (search for similar items in EconPapers)
Date: 2022
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/S0306261922003853
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:appene:v:314:y:2022:i:c:s0306261922003853

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2022.118975

Access Statistics for this article

Applied Energy is currently edited by J. Yan

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