In the context of dynamic pricing, reliable and precise demand-side electricity price forecasting is crucial for effective energy management. We propose a new electricity price forecasting model that delivers consistently accurate predictions. The underlying prediction framework decomposes the time series via a wavelet transform before applying Long Short-Term Memory for forecasting. Earlier research using this framework has consistently applied the same mother wavelet for decomposition, without variation. Nevertheless, this fixed approach struggles to adapt to the daily or seasonal fluctuations inherent in the time series. To address this limitation, we introduce periodic switching of the mother wavelet, effectively enabling flexible modification of the decomposition technique and yielding stable, high-accuracy electricity price forecasting. Experimental evaluation revealed that the model enhanced prediction accuracy by up to 42.8% relative to the version employing a fixed mother wavelet. The findings confirm that this flexible forecasting approach reliably produces highly accurate results.
As electric vehicles (EVs) steadily gain popularity on public roads, commercial EV charging has become a routine business service. As this trend continues, charging station operators are actively exploring strategies to boost the profitability of their services or to distribute limited resources more effectively. Dynamic pricing has proven effective for increasing revenue in markets with varying customer demand. This paper introduces a Markov Decision Process (MDP)-based framework for dynamic pricing that maximizes either revenue or utilization for charging station operators. The proposed method is implemented through a Monte Carlo Tree Search (MCTS) algorithm. It is tested in simulation across multiple scenarios derived from a real-world dataset of EV charging sessions. Results demonstrate that the approach delivers near-optimal pricing decisions within milliseconds, even for large-scale instances, delivering substantial improvements in revenue or utilization compared to a conventional flat-rate baseline across various parameter settings.
Institute of Smart Energy Systems Press