Search Results for author:
Found 11 papers, 2 papers with code
Enhancing Policy Gradient with the Polyak Step-Size Adaption
Policy gradient is a widely utilized and foundational algorithm in the field of reinforcement learning (RL).
Dynamic and Memory-efficient Shape Based Methodologies for User Type Identification in Smart Grid Applications
Detecting behind-the-meter (BTM) equipment and major appliances at the residential level and tracking their changes in real time is important for aggregators and traditional electricity utilities.
A New Time Series Similarity Measure and Its Smart Grid Applications
As a result, there is a need for a new distance measure that can quantify both the amplitude and temporal changes of electricity time series for smart grid applications, e. g., demand response and load profiling.
Modelling Irrational Behaviour of Residential End Users using Non-Stationary Gaussian Processes
Demand response (DR) plays a critical role in ensuring efficient electricity consumption and optimal use of network assets.
A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence
Modern policy optimization methods in reinforcement learning, such as TRPO and PPO, owe their success to the use of parameterized policies.
Comparison and Evaluation of Methods for a Predict+Optimize Problem in Renewable Energy
As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area.
Optimal activity and battery scheduling algorithm using load and solar generation forecasts
Energy usage optimal scheduling has attracted great attention in the power system community, where various methodologies have been proposed.
Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies
We consider infinite-horizon discounted Markov decision processes and study the convergence rates of the natural policy gradient (NPG) and the Q-NPG methods with the log-linear policy class.
Optimal activity and battery scheduling algorithm using load and solar generation forecast
In this report, we provide a technical sequence on tackling the solar PV and demand forecast as well as optimal scheduling problem proposed by the IEEE-CIS 3rd technical challenge on predict + optimize for activity and battery scheduling.
IRMAC: Interpretable Refined Motifs in Binary Classification for Smart Grid Applications
In this paper, we seek to identify residential consumers based on their BTM equipment, mainly rooftop photovoltaic (PV) systems and electric heating, using imported/purchased energy data from utility meters.
A general sample complexity analysis of vanilla policy gradient
We then instantiate our theorems in different settings, where we both recover existing results and obtain improved sample complexity, e. g., $\widetilde{\mathcal{O}}(\epsilon^{-3})$ sample complexity for the convergence to the global optimum for Fisher-non-degenerated parametrized policies.
