This chapter provides an overview about forecast models on temporal and spatial scales to enable smart methodologies for design and control. In order to succeed in this scope, a number of IoT components, such as distributed sensors, actuators, as well as decision-making devices are necessary. Additionally, by integrating smart grid and energy forecast with big data analytics and deep learning services, it enables to produce accurate and detailed local forecasts, in order to control the grid dynamically.
CITATION STYLE
Anagnostos, D. (2019). Solar energy forecasting in the era of IoT enabled smart grids. In Power Systems (pp. 133–146). Springer Verlag. https://doi.org/10.1007/978-3-030-03640-9_7
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