The mainstream of EU policies is heading towards the conversion of the nowadays electricity consumer into the future electricity prosumer (producer and consumer) in markets in which the production of electricity will be more local, renewable and economically efficient. One key component of a local short-term and medium-term planning tool to enable actors to efficiently interact in the electric pool markets is the ability to predict and decide on forecast prices. Given the progressively more important role of renewable production in local markets, we analyze the influence of renewable energy production on the electricity price in the Iberian market through historical records. The dependencies discovered in this analysis will serve to identify the forecasts to use as explanatory variables for an electricity price forecasting model based on recurrent neural networks. The results will show the wide impact of using forecasted renewable energy production in the price forecasting.

This paper introduces the Temporal Inference Problem (TIP), a general formulation for a family of inference problems that reason about the past, present or future state of some observed agent. A TI...

In real-world scenarios, the successful execution of an agent’s planned actions is not always guaranteed, as actions may fail in unpredictable ways that are not explicitly modeled. To address this challenge, the concept of Resilient Planning and the RESPLAN framework were introduced focusing on the generation of k-resilient plans that enable an agent to reach its goals even in the presence of up to k execution failures. In this paper, we propose a new version of the RESPLAN planning algorithm based on two significant enhancements. The first incorporates landmarks into a pruning strategy, enabling the planner to avoid unnecessary explorations and yielding substantial performance gains, especially when no resilient plan exists. The second introduces a planning adaptation strategy exploiting regressed state formulas to support the search process during (re)planning, reducing the number of iterations required when a resilient plan does exist. We compare our methods against RESPLAN and other baselines, demonstrating substantial improvements across multiple domains.