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Found 6 papers, 2 papers with code
Practical Framework for Problem-Based Learning in an Introductory Circuit Analysis Course
Introductory courses on electric circuits at undergraduate level are usually presented in quite abstract terms, with questions and problems quite far from practical problems.
Decision-Oriented Learning for Future Power System Decision-Making under Uncertainty
This paper first elaborates on the mismatch between more accurate forecasts and more optimal decisions in the power system caused by statistical-based learning (SBL) and explains how DOL resolves this problem.
Prescribing net demand for two-stage electricity generation scheduling
Standard industry practice deals with the uncertain net demand in the forward stage by replacing it with a good estimate of its conditional expectation (usually referred to as a point forecast), so as to minimize the need for balancing power in real time.
Forecasting the Price-Response of a Pool of Buildings via Homothetic Inverse Optimization
Specifically, we assume that the aggregate power is a homothet of a prototype building, whose physical and technical parameters are chosen to be the mean of those in the pool.
A novel embedded min-max approach for feature selection in nonlinear support vector machine classification
In recent years, feature selection has become a challenging problem in several machine learning fields, such as classification problems.
Feature-driven Improvement of Renewable Energy Forecasting and Trading
Inspired from recent insights into the common ground of machine learning, optimization and decision-making, this paper proposes an easy-to-implement, but effective procedure to enhance both the quality of renewable energy forecasts and the competitive edge of renewable energy producers in electricity markets with a dual-price settlement of imbalances.
