Open AccessA Taxonomy of electricity demand forecasting techniques and a selection strategy
Author(s) -
Paraschos Maniatis
Publication year - 2017
Publication title -
international journal of management excellence
Language(s) - English
Resource type - Journals
ISSN - 2292-1648
DOI - 10.17722/ijme.v8i2.874
Subject(s) - exponential smoothing , autoregressive integrated moving average , computer science , demand forecasting , electricity , adaptability , operations research , kalman filter , electricity demand , artificial intelligence , machine learning , time series , electricity generation , economics , engineering , power (physics) , physics , electrical engineering , management , quantum mechanics , computer vision
In this research, a taxonomy of known electricity demand forecasting techniques is presented based on extensive empirical studies. In addition, a decision strategy for selecting an electricity demand forecasting method has been presented. The strategy has been formulated based on an eight-factor model created by World Bank and inputs gathered from electricity demand forecasting experts (through a questionnaire). The techniques have been assessed based on time horizon, accuracy, complexity, skill level, data volumes, geographical coverage, adaptability, and cost. The experts rated ARIMA (Autoregressive integrated moving average) with exponential smoothing and Kalman filtering as the most adopted method. The next most adopted method is Artificial Neural Networks with preprocessed Linear and Fuzzy inputs. However, now Support Vector Regression may replace this method, which is currently tested by many electrical engineers engaged in electricity demand forecasting. In addition to these highlighted methods, this research also presents the ratings of other techniques based on the eight-factor model of World Bank.