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The structural changes in the energy sector caused by renewable sources and digitization have resulted in an increased use of Artificial Intelligence (AI), including Machine Learning (ML) models. However, these models' black-box nature and complexity can create issues with transparency and trust, thereby hindering their interpretability. The use of Explainable AI (XAI) can offer a solution to these challenges. This paper explores the application of an XAI-based framework to analyze and evaluate a photovoltaic energy generation forecasting problem and contribute to the trustworthiness of ML solutions.

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Automatic energy management systems allow users' active participation in flexibility management while assuring their energy demands. We propose a transparent framework for automated energy management to increase trust and improve the learning process, combining machine learning, experts' knowledge, and semantic reasoning. A practical example of thermal comfort shows the advantages of the framework.

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Renewable energy sources have transformed the electricity market, allowing virtual power players or aggregators to participate and benefit from selling surplus energy. However, meeting demand and ensuring energy production stability can be challenging due to the intermittent nature of renewable sources. Accurate forecasting of energy consumption, generation, and electricity prices is critical to address these issues. Moreover, the selection of the best algorithm for forecasting is usually based on the predictions’ accuracy, neglecting other factors such as the impact of errors on the real context. This paper presents a study on the economic risk of price forecasting errors on the electricity market’s trading. For this, a simulation model is proposed to analyze the deviations between actual and predicted prices and how these deviations can affect trading in the electricity market, where the main purpose is to maximize profit, depending on whether the player is buying or selling electricity. The economic risk analysis and the predictions scores are used to improve the forecasts, using an approach based on reinforcement learning to evaluating and selecting which models demonstrated better performance in past transactions. The study involved simulating an aggregator’s transactions in the Iberian electricity market for two consecutive days in October 2021. Real data from the market operator between 2020 and 2021 and seven forecasting models were used. The findings showed that errors have a significant impact on profit. Including the economic impact analysis and score evaluation of forecasting methods to determine which method can offer better results has proven to be a viable approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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In recent years, sustainable power supply has become a necessary asset for the daily survival and development of populations. The incentive to the use of renewable energies has been increasing worldwide. Solar energy, in particular, is widespreading fast in countries whose location allows to obtain excellent radiation conditions. In this work, autonomous photovoltaic (PV) systems are studied, having as main aim its application in the supply of urban loads. For this purpose, a PV system is designed to supply the decorative lighting of a monument. Particular emphasis is given to studying the behavior of the energy storage system. The achieved results demonstrate that the use of this type of systems is a very efficient solution for the municipalities to supply several urban loads such as fountains, traffic lights, decorative lighting, among others. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.