Hardware implementation of the upper confidence-bound algorithm for reinforcement learning

Radovic, Nevena; Erceg, Milena

doi:10.1016/j.compeleceng.2021.107537

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…As for S, it determines the next point to query by selecting the most promising candidate. Normally, three acquisition functions are widely used, which are the maximum probability of improvement (MPI) [35], expected improvement (EI) [36], and upper confidence bound (UCB) [37]. The disadvantage of MPI is that it only chooses the points with highly confident to query, hence there is little improvement of the model.…”

Section: Forward Layermentioning

confidence: 99%

Hybrid Short-term Load Forecasting Method Based on Empirical Wavelet Transform and Bidirectional Long Short-term Memory Neural Networks

Zhang

Kuenzel

Colombo

et al. 2022

Journal of Modern Power Systems and Clean Energy

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Accurate short-term load forecasting is essential to modern power systems and smart grids. The utility can better implement demand-side management and operate power system stably with a reliable load forecasting system. The load demand contains a variety of different load components, and different loads operate with different frequencies. The conventional load forecasting methods, e.g., linear regression (LR), auto-regressive integrated moving average (ARIMA), deep neural network, ignore the frequency domain and can only use time-domain load demand as inputs. To make full use of both time-domain and frequency-domain features of the load demand, a load forecasting method based on hybrid empirical wavelet transform (EWT) and deep neural network is proposed in this paper. The proposed method first filters noises via wavelet-based denoising technique, and then decomposes the original load demand into several sub-layers to show the frequency features while the time-domain information is preserved as well. Then, a bidirectional long short-term memory (LSTM) method is trained for each sub-layer independently. In order to better tune the hyperparameters, a Bayesian hyperparameter optimization (BHO) algorithm is adopted in this paper. Three case studies are designed to evaluate the performance of the proposed method. From the results, it is found that the proposed method improves the prediction accuracy compared with other load forecasting method.

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Section: Forward Layermentioning

confidence: 99%