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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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A Reinforcement Learning approach for the continuous electricity market ofGermany: Trading from the perspective of a wind park operator

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能源与人工智能(英文) 2022年第2期8卷 67-78页

作者： Malte Lehna Björn Hoppmann Christoph Scholz RenéHeinrich Fraunhofer Institute for Energy Economics and Energy System Technology(IEE) Königstor 5934119 KasselGermany

With the rising extension of renewable energies, the intraday electricity markets have recorded a growingpopularity amongst traders as well as electric utilities to cope with the induced volatility of the energysupply. Through their short trading horizon and continuous nature, the intraday markets offer the abilityto adjust trading decisions from the day-ahead market or reduce trading risk in a short-term notice. Producersof renewable energies utilize the intraday market to lower their forecast risk, by modifying their providedcapacities based on current forecasts. However, the market dynamics are complex due to the fact that thepower grids have to remain stable and electricity is only partly storable. Consequently, robust and intelligenttrading strategies are required that are capable to operate in the intraday market. In this work, we proposea novel autonomous trading approach based on Deep Reinforcement Learning (DRL) algorithms as a possiblesolution. For this purpose, we model the intraday trade as a Markov Decision Process (MDP) and employ theProximal Policy Optimization (PPO) algorithm as our DRL approach. A simulation framework is introducedthat enables the trading of the continuous intraday price in a resolution of one minute steps. We test ourframework in a case study from the perspective of a wind park operator. We include next to general tradeinformation both price and wind forecasts. On a test scenario of German intraday trading results from 2018,we are able to outperform multiple baselines with at least 45.24% improvement, showing the advantage of theDRL algorithm. However, we also discuss limitations and enhancements of the DRL agent, in order to increasethe performance in future works.

关键词： Deep Reinforcement Learning German intraday electricity trading Deep neural networks Markov Decision Process Proximal Policy Optimization electricity price forecast

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