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Found 10 papers, 3 papers with code
Tackling Missing Values in Probabilistic Wind Power Forecasting: A Generative Approach
Although it is natural to consider addressing this issue by imputing missing values before model estimation and forecasting, we suggest treating missing values and forecasting targets indifferently and predicting all unknown values simultaneously based on observations.
Deriving Loss Function for Value-oriented Renewable Energy Forecasting
By integrating it into the upper-level objective for minimizing expected operation cost, we convert the bilevel problem to a single-level one and derive the loss function for training the model.
Fast Constraint Screening for Multi-Interval Unit Commitment
Extensive simulations on both specific load samples and load regions validate the proposed technique can screen out more than 80% constraints while preserving the feasibility of multi-interval UC problem.
Toward Value-oriented Renewable Energy Forecasting: An Iterative Learning Approach
To bridge the gap, we design a value-oriented point forecasting approach for sequential energy dispatch problems with renewable energy sources.
Enabling Fast Unit Commitment Constraint Screening via Learning Cost Model
Unit commitment (UC) are essential tools to transmission system operators for finding the most economical and feasible generation schedules and dispatch signals.
Targeted Demand Response: Formulation, LMP Implications, and Fast Algorithms
Numerical studies demonstrate compared with the benchmarking strategy, the proposed approach can reduce the price to the reference point with less efforts in demand reduction.
A Contextual Bandit Approach for Value-oriented Prediction Interval Forecasting
The numerical study regarding a two-timescale operation of a virtual power plant verifies the superiority of the proposed approach in terms of operational value.
Continuous and Distribution-free Probabilistic Wind Power Forecasting: A Conditional Normalizing Flow Approach
It relies on a base distribution and a set of bijective mappings.
Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement Learning
Case studies on both load and net load demonstrate that the proposed method can better adapt to data distribution compared with online central PIs method.
Skeleton Sequence and RGB Frame Based Multi-Modality Feature Fusion Network for Action Recognition
To solve the problem, we propose a multi-modality feature fusion network to combine the modalities of the skeleton sequence and RGB frame instead of the RGB video, as the key information contained by the combination of skeleton sequence and RGB frame is close to that of the skeleton sequence and RGB video.
