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Found 19 papers, 7 papers with code
Clustering of timed sequences -- Application to the analysis of care pathways
These methods are then applied in clustering algorithms to propose original and sound clustering algorithms for timed sequences.
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.
Seamless and multi-resolution energy forecasting
The relationship between (i) errors in both time and frequency domains and (ii) operational value of the forecasts is analysed.
Privacy-Aware Data Acquisition under Data Similarity in Regression Markets
Data markets facilitate decentralized data exchange for applications such as prediction, learning, or inference.
Bayesian Regression Markets
Machine learning tasks are vulnerable to the quality of data used as input.
Towards Replication-Robust Analytics Markets
Many industries rely on data-driven analytics, yet useful datasets are often distributed amongst market competitors that are reluctant to collaborate and share information.
On tracking varying bounds when forecasting bounded time series
We consider a new framework where a continuous, though bounded, random variable has unobserved bounds that vary over time.
On the contribution of pre-trained models to accuracy and utility in modeling distributed energy resources
Despite their growing popularity, data-driven models of real-world dynamical systems require lots of data.
What may future electricity markets look like?
Challenges include accommodation of renewable energy generation, decentralization and support to investment, while opportunities are mainly that advances in technical and social sciences provide us with many more options in terms of future market design.
Online Decision Making for Trading Wind Energy
We propose and develop a new algorithm for trading wind energy in electricity markets, within an online learning and optimization framework.
Strategic Coalition for Data Pricing in IoT Data Markets
Finally, we reveal the structure of the formulated problem as a distributed coalition game and solve it following the simplified split-and-merge algorithm.
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.
A Market for Trading Forecasts: A Wagering Mechanism
This market delivers an aggregated forecast to the buyer (pre-event) and allocates a payoff to the sellers (post-event) for their contribution.
Reliability-Aware Probabilistic Reserve Procurement
The proposed probabilistic reserve procurement allows restricted reserve providers to enter the market, thereby increases liquidity and has the potential to lower procurement costs in power systems with high shares of variable renewable energy sources.
Moving from Linear to Conic Markets for Electricity
We propose a new forward electricity market framework that admits heterogeneous market participants with second-order cone strategy sets, who accurately express the nonlinearities in their costs and constraints through conic bids, and a network operator facing conic operational constraints.
Monetizing Customer Load Data for an Energy Retailer: A Cooperative Game Approach
When energy customers schedule loads ahead of time, this information, if acquired by their energy retailer, can improve the retailer's load forecasts.
Optimization and Control Systems and Control Systems and Control Mathematical Finance
Stochastic Control and Pricing for Natural Gas Networks
Furthermore, the chance-constrained optimization provides the foundation of a stochastic pricing scheme for natural gas networks, which improves on a deterministic market settlement by offering the compensations to network assets for their contribution to uncertainty and variance control.
Optimization and Control
Differentially Private Convex Optimization with Feasibility Guarantees
This paper develops a novel differentially private framework to solve convex optimization problems with sensitive optimization data and complex physical or operational constraints.
A Critical Overview of Privacy-Preserving Approaches for Collaborative Forecasting
Cooperation between different data owners may lead to an improvement in forecast quality - for instance by benefiting from spatial-temporal dependencies in geographically distributed time series.
