A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models
Expert Systems with Applications: An International Journal
A framework for short-term activity-aware load forecasting
Joint Proceedings of the Workshop on AI Problems and Approaches for Intelligent Environments and Workshop on Semantic Cities
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Short-term load forecasting is achieved using a lifting scheme and autoregressive integrated moving average (ARIMA) models. The lifting scheme is a general and flexible approach for constructing bi-orthogonal wavelets that are usually in the spatial domain. The lifting scheme is embedded into the ARIMA models to enhance forecasting accuracy. Based on wavelet multi-revolution analysis (MRA) results, the lifting scheme decomposes the original load series into different sub-series at different revolution levels, which display the different frequency characteristic of a load. The sub-series are then forecast using properly fitted ARIMA models. Finally, forecasting results at different levels are reconstructed to generate an original load prediction by the inverse lifting scheme. In this study, the Coeflet 12 wavelet is factored into lifting scheme steps. The proposed algorithm was tested by applying it to different practical load data types from the Taipower Company in 2007 for one-day-ahead load forecasting. Simulation results indicate that the forecasting performance of the proposed approach is superior to that of the back-propagation network (BPN) algorithm and traditional ARIMA models.