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Electricity load forecasting is a key task in the planning and operation of power systems and electricity markets, and its importance increases with the advent of smart grids. In this paper, we present AWNN, a new approach for very short-term load forecasting. AWNN decomposes the complex electricity load data into components with different frequencies that are predicted separately. It uses an advanced...
Appropriate feature (variable) selection is crucial for accurate forecasting. In this paper we consider the task of forecasting the future electricity load from a time series of previous electricity loads, recorded every 5min. We propose a two-step approach that identifies a set of candidate features based on the data characteristics and then selects a subset of them using correlation and instance-based...
We present a new approach for electricity load forecasting based on non-decimated multilevel wavelet transform, in combination with two-stage feature selection and machine learning prediction algorithm. The key idea is to decompose the non-stationary and noisy electricity load data into sub-series of different frequencies, analyse and predict them separately. The feature selection integrates autocorrelation...
We present new approaches for 5-minute ahead electricity load forecasting. They were evaluated on data from the Australian electricity market operator for 2006-2008. After examining the load characteristics using autocorrelation analysis with 4-week sliding window, we selected 51 features. Using this feature set with linear regression and support vector regression we achieved an improvement of 7.56%...
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