Machine learning and agent-based modeling are two popular tools in energy research. In this article, we propose an innovative methodology that combines these methods. For this purpose, we develop an electricity price forecasting technique using artificial neural networks and integrate the novel...

The penetration of distributed generation, energy storages and smart loads has resulted in the emergence of prosumers: entities capable of adjusting their electricity production and consumption in order to meet environmental goals and to participate profitably in the available electricity...

The penetration of distributed generation, energy storages and smart loads has resulted in the emergence of prosumers: entities capable of adjusting their electricity production and consumption in order to meet environmental goals and to participate profitably in the available electricity...

Electricity price forecasting has become an area of increasing relevance in recent years. Despite the growing interest in predictive algorithms, the challenges are difficult to overcome given the restricted access to relevant data series and the lack of accurate metrics. Multiple models have...