Weighted subsethood and reasoning based fuzzy time series for moving holiday electricity load demand forecasting

• Auteur principal: Rosnalini, Mansor
• Format: Thèse
• Langue: anglais; anglais
• Publié: 2021
• Sujets: TK Electrical engineering. Electronics Nuclear engineering
• Accès en ligne: https://etd.uum.edu.my/9548/1/depositpermission-not%20allow_s94930.pdf; https://etd.uum.edu.my/9548/2/s94930_01.pdf; https://etd.uum.edu.my/9548/

A Moving holiday is a non-fixed holiday according to the Gregorian calendar. Most of the electricity load demand studies showed that this event affects the accuracy of load forecasting. It is due to a limited historical data about moving holiday, and a longer time series is acquired to reveal the pattern. Besides, different characteristics of each moving holiday and existence of a great number of irregularities in the load data also contribute to the forecasting inaccuracy and uncertainty. Fuzzy time series (FTS) algorithm is able to overcome moving holiday electricity load demand (MH-ELD) forecasting problem, but the FTS algorithm lacks final model interpretation, less interpretability of fuzzy logical relationship strength, and does not provide a complete FTS forecasting process. These will provide less information about the relationship that naturally represents how humans make judgments and decisions, and less guide to conduct complete FTS forecasting process. Therefore, this study modified the conventional FTS algorithm by applying weighted subsethood in the algorithm on segmented Malaysia electricity load demand time series data. The modified algorithm, Weighted Subsethood Segmented Fuzzy Time Series (WeSuSFTS) consists of four main phases; data pre-processing, model development, model implementation and model evaluation. The WeSuSFTS algorithm uses the min-max operator for fuzzy reasoning and average rule defuzzification which make the process simpler. Two types of WeSuSFTS: One-factor and M-factor were also executed. The results show that the WeSuSFTS models have higher accuracy compared to the conventional FTS models, particularly the One-factor model gives the most outstanding forecasting results with the smallest mean absolute percentage error. Hence, the WeSuSFTS models succeed to improve the MH-ELD forecasting accuracy.