Explainergy: Towards Explainability of Metaheuristic Performance in the Energy Field

We propose the concept of “explainergy”, a new way of including explainability in the metaheuristic performance of algorithms solving problems in the energy domain. To this end, we open the discussion around eXplainable computational Intelligence (XCI), focusing on using metaheuristic optimization for complex energy-related problems. It is well known that computational intelligence applied to optimization cannot guarantee optimality theoretically and also faces issues related to premature convergence, tuning parameters, and variability of the results. These aspects slow the adoption of such methods by energy industry practitioners. Our proposal considers incorporating ideas already applied to the artificial intelligence paradigm, namely those related to eXplainable AI, to motivate current research in this field and provide solutions from metaheuristics with explainability character-istics. Through a case study solving a bidding problem in local electricity markets, we shed light on some ideas that might be advantageous to understanding the metaheuristic performance for energy experts unfamiliar with approximate algorithms. If an XCI framework is successfully developed, it can increase metaheuristic adoption, reliability, and broader success.