Dear Colleagues,

Load forecasting is an important tool in smart energy management systems, smart grids and micro-grids. Forecasting energy demand is still a challenge, requiring information such as the behavior of inidividual users, weather forecasting, and econometric variables, all to provide accurate predictions of residential, commercial and industrial consumption patterns. The ultimate goal is that energy providers can improve their operations, reduce costs and provide better and more reliable services.

To this end, many researchers are developing artificial intelligence (AI)-based solutions that offer load forecasting to optimize operation and planning of energy systems. There are several statistical models for modeling and forecasting time series and many computational intelligence (CI)  approaches, such as machine learning techniques, deep learning and hybrid models combining these with fuzzy systems and genetic algorithms. In this Special Issue, we intend to gather the latest contributions in CI-based approaches for energy load forecasting in smart energy management grids.

In the context of smart grids, the wide use of electronic sensors and continuous monitoring leads to a scenario of large-scale time series data and large volumes of data, which can be used in prediction and monitoring applications. Therefore, scalable and efficient models are required, working in an increasingly dynamic and uncertain environment, aggregating forecasting capabilities with large volumes and streams of data, multivariate time series data and non-stationary data.

Furthermore, the vast majority of published methods make use of statistical and machine learning techniques that are difficult to interpret. In a context in which the use of such models can define public policies for energy investment, operation and pricing, their intelligibility and transparency becomes increasingly important, highlighting the need for CI-based explainable models.

Prof. Dr. Frederico Gadelha Guimarães
Prof. Dr. Petrônio Cândido de Lima e Silva
Guest Editors

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• smart grids
• smart energy systems
• smart homes
• smart buildings
• energy load forecasting
• electricity load forecasting
• time series forecasting
• machine learning
• artificial neural networks
• deep learning
• fuzzy time series
• fuzzy systems