Priority index considering temperature and date proximity for selection of similar days in knowledge-based short term load forecasting method. (1st February 2018)

• Record Type: Journal Article
• Authors: Karimi, M.; Karami, H.; Gholami, M.; Khatibzadehazad, H.; Moslemi, N.
• Is Part Of: Energy. Volume 144(2018)
• Page Start: 928
• Page End: 940
• Publication Date: 2018-02-01
• Subjects: Short term load forecasting -- Similar day selection -- Power system partitioning effect -- Priority index -- Knowledge-based forecasting; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2017.12.083
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Short term load forecasting (STLF) is one of the important issues in the energy management of power systems. Increasing the accuracy of STLF results leads to improving the energy system scheduling and decreasing the operating costs. Different methods have been proposed and applied in the STLF problem such as neural network, fuzzy system, regression-based and neuro-fuzzy methods. This paper investigates the knowledge-based method that has less computation time and memory compared with other methods. The accuracy of knowledge-based STLF method is improved by proposing a novel priority index for selection of similar days. In this index, temperature similarity and date proximity are simultaneously considered. In order to consider the effect of temperature in STLF more efficiently, the system is partitioned into the smaller regions and the STLF of the whole system is calculated by gathering the STLF of all regions. The proposed method is implemented on a sample real data, Iran's national power network, to show the advantages of the proposed method compared with Bayesian neural network and locally linear neuro-fuzzy methods in aspects of accuracy and computation time. It is shown that the proposed method decreases yearly mean absolute percentage error (MAPE), and generates more reliable load forecasting. Highlights: Proposing priority index for similar-day selection in knowledge-based STLF. Considering temperature and date proximity in the priority index. Considering temperature more efficiently by partitioning the large geographical system. More accurate STLF results by defining and combination of two historical data sets.