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Flexible electricity price forecasting by switching mother wavelets based onwavelet transform and Long Short-Term Memory

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能源与人工智能(英文) 2022年第4期10卷 95-102页

作者： Koki Iwabuchi Kenshiro Kato Daichi Watari Ittetsu Taniguchi Francky Catthoor Elham Shirazi takao onoye Osaka University 1-5 YamadaokaSuitaOsaka 565-0871Japan imec Kapeldeef 753001 HeverleeBelgium ESAT KULeuvenKasteelpark Arenberg 103001 HeverleeBelgium EnergyVille Dennenstraat 7B-3600 GenkBelgium University of Twente Enschede7500 AENetherlands

Under dynamic pricing, stable and accurate electricity price forecasting on the demand side is essential forefficient energy management. We have developed a new electricity price forecasting model that providesconsistently accurate forecasts. The base prediction model decomposes the time series using wavelet transformand then predicts it by Long Short-Term Memory. Previous studies using this model have always decomposedtime series in the same way without changing the mother wavelet. However, this makes it difficult to respond tochanges in time series that vary daily or seasonally. Therefore, we periodically switch the mother wavelet, i.e.,flexibly change the time series decomposition method, to achieve stable and highly accurate electricity priceforecasting. In an experiment, the model improved prediction accuracy by up to 42.8% compared to predictionwith a fixed mother wavelet. Experimental results show that the proposed flexible forecasting method canconsistently provide highly accurate forecasts.

关键词： Dynamic pricing Electricity price forecast Wavelet transform Long Short-Term Memory neural network

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Hierarchical Object-Oriented Video Segmentation and Representation Algorithm

Hierarchical Object-Oriented Video Segmentation and Represen...

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1998 Fourth International Conference Signal Processing

作者： Jianping FAN Gen FUJITA Jun YU Koji MIYANOHANA takao onoye Nagisa ISHIURA Lide WU Isao SHIRAKAWA Department of Information Systems Engineering Faculty of EngineeringOsaka UniversityYamadaoka 2-1SuitaOsaka 565Japan Department of Computer Science Fudan UniversityShanghai 200433China

＜正＞In this paper,a novel object-oriented hierarchical video segmentation and representation algorithm is proposed based on four-component video model,where the local variance contrast and the frame difference contrast are selected for generating the 2D spatiotemporal entropy. The extracted object is first represented by a group of（4 x 4） blocks coarsely,then the intra-block edge extraction on edge blocks and the joint spatiotemporal similarity test among neighboring blocks are further performed for determining the meaningful real objects. This proposed hierarchical segmentation algorithm may be very useful for MPEG-4 applications.A novel fast algorithm is also introduced for reducing the search burden. Moreover,this unsupervised algorithm also makes the automatic image and video segmentation possible.

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