21 citations · 31 across the 8 of their papers we have counts for
8 papers
Behaviour-Conditioned Neural Processes for Adaptive Residential Short-Term Load Forecasting
Ramin Soleimani, Andrea Visentin, Dirk Pesch
Residential short-term load forecasting (STLF) is challenging because household demand is heterogeneous, temporally variable, and shaped by diverse behavioural routines. This work…
The Evolution of Probabilistic Price Forecasting Techniques: A Review of the Day-Ahead, Intra-Day, and Balancing Markets
Ciaran O'Connor, Mohamed Bahloul, Steven Prestwich +1
Electricity price forecasting has become a critical tool for decision-making in energy markets, particularly as the increasing penetration of renewable energy introduces greater vo…
Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market
Ciaran O'Connor, Mohamed Bahloul, Roberto Rossi +2
The integration of renewable energy into electricity markets poses significant challenges to price stability and increases the complexity of market operations. Accurate and reliabl…
Optimizing Quantile-based Trading Strategies in Electricity Arbitrage
Ciaran O'Connor, Joseph Collins, Steven Prestwich +1
Efficiently integrating renewable resources into electricity markets is vital for addressing the challenges of matching real-time supply and demand while reducing the significant e…
Electricity Price Forecasting in the Irish Balancing Market
Ciaran O'Connor, Joseph Collins, Steven Prestwich +1
Short-term electricity markets are becoming more relevant due to less-predictable renewable energy sources, attracting considerable attention from the industry. The balancing marke…
Forecasting Workload in Cloud Computing: Towards Uncertainty-Aware Predictions and Transfer Learning
Andrea Rossi, Andrea Visentin, Diego Carraro +2
Predicting future resource demand in Cloud Computing is essential for optimizing the trade-off between serving customers' requests efficiently and minimizing the provisioning cost.…
Stochastic Dynamic Programming Heuristic for the (R, s, S) Policy Parameters Computation
Andrea Visentin, Steven Prestwich, Roberto Rossi +1
The (R, s, S) is a stochastic inventory control policy widely used by practitioners. In an inventory system managed according to this policy, the inventory is reviewed at instant R…
Computing Optimal (R, s, S) Policy Parameters by a Hybrid of Branch-and-Bound and Stochastic Dynamic Programming
Andrea Visentin, Steven Prestwich, Roberto Rossi +1
A well-know control policy in stochastic inventory control is the (R, s, S) policy, in which inventory is raised to an order-up-to-level S at a review instant R whenever it falls b…