Abstract
![CDATA[Short term load forecasting plays an important role for the energy industry as accurate predictions are vital in utilizing available resources to optimize the electricity production. Literature reveals that commonly used approaches for such predictions are done using multiple regression, stochastic time series, exponential smoothing, state-space models, neural network models and fuzzy logic, to name a few. In the recent past however, functional data analysis has been an emerging trend to analyse time related data. The electricity demand for a particular day varies with time and as such the daily load curve can be modelled using functional data analysis. Instead of using hourly electricity demand of each day, the dimensionality of the dataset could be reduced using functional principal component analysis. The principal component scores of the selected components were used for prediction. Seasonality and speciality of the day were incorporated using dummy variables and ARIMA models with regressors have been used to predict the principal component scores. Significant variables for the model were identified prior to ARIMA modelling and residual analysis was performed to validate the fitted models. From the predicted principal component scores, the next day’s load curve is evaluated. A moving window has been used to make the predictions real time and for the prediction process to be more efficient. This proposed functional analysis based methodology when compared with a commonly used error back-propagated neural network approach have shown improved predictions of the forecasted electricity demands.]]
Original language | English |
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Title of host publication | Proceedings of the 1st International Conference on Machine Learning and Data Engineering (iCMLDE 2017), 20-22 November 2017, Sydney, Australia |
Publisher | Global Circle for Scientific, Technological and Management Research |
Pages | 76-82 |
Number of pages | 7 |
ISBN (Print) | 9780648014737 |
Publication status | Published - 2017 |
Event | International Conference on Machine Learning and Data Engineering - Duration: 3 Dec 2018 → … |
Conference
Conference | International Conference on Machine Learning and Data Engineering |
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Period | 3/12/18 → … |
Keywords
- electricity
- forecasting