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The performance of SVM highly depends on the selection of parameters, and Gaussian disturbance Firefly Algorithm (GDFA) proposed in this study can satisfy the necessary. Ensemble Empirical Mode Decomposition (EEMD) is employed to decompose the load data into sub-series with different frequency. This paper extracts daily maximum temperature, daily minimum temperature, wind speed, rainfall, day type, and the load one week before the forecasting day as input variables. Two cases are taken to verify the effective performance of GDFA compared with FA, as well as the superiority of EEMD-GDFA-SVM over other forecasting techniques in short-term load forecasting.<\/jats:p>","DOI":"10.3233\/jifs-152081","type":"journal-article","created":{"date-parts":[[2016,7,22]],"date-time":"2016-07-22T10:54:55Z","timestamp":1469184895000},"page":"1709-1719","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["Improved short-term load forecasting based on EEMD, Guassian disturbance firefly algorithm and support vector machine"],"prefix":"10.1177","volume":"31","author":[{"given":"Zongyun","family":"Song","sequence":"first","affiliation":[{"name":"School of Economic and Management, North China Electric Power University, Hui Longguan, Chang Ping District, Beijing, 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