Online Adaptive Learning in Energy Trading Stackelberg Games with Time-Coupling Constraints

Abstract

In this work, a Stackelberg game theoretic framework is proposed for trading energy bidirectionally between the demand-response (DR) aggregator and the prosumers. This formulation allows for flexible energy arbitrage and additional monetary rewards while ensuring that the prosumers' desired daily energy demand is met. Then, a scalable (with the number of prosumers) approach is proposed to find approximate equilibria based on online sampling and learning of the prosumers' cumulative best response. Moreover, bounds are provided on the quality of the approximate equilibrium solution. Last, real-world data from the California day-ahead energy market and the University of California at Davis building energy demands are utilized to demonstrate the efficacy of the proposed framework and the online scalable solution.

Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data

“…Several machine learning applications are implemented as a workflow, that starts with data collection and ends with a model evaluation and simulations or software development. Examples of fields that introduce custom machine learning workflow solutions, include but are not limited to, malware detection and classification [1], software development with adversarial attack classification [2], task fault prediction in workflows developed with cloud services [3], pipeline optimization [4], classification of forest stand species via remote sensing [5], detection of mechanical discontinuities in materials and prediction of martensitic transformation peak temperature of alloys [6,7], optimization of metabolic pathways and ranking of miRNAs retards insulin gene transcription in human islets [8,9], large-scale crop yield forecasting [10], classification and forecasting in chemical engineering [11], predictive modeling in medicine [12], protein engineering/biophysical screening in pharmaceutical sciences [13], forecasting of oil uptake in batter for food science [14], vegetation height classification, forecasting fractured coal seams, and climate-related forecasting in environmental sciences [15][16][17], energy systems controlled by occupancy detection or energy demand forecasting [18][19][20][21][22], as well as environmental impact estimation from commercial aviation and aerospace requirements Disclaimer/Publisher's Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions, or products referred to in the content.…”