New journal publication: “Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants”

Now you can check out our latest pubication in the Energies journal (Q2): “Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants”. Here, Jesus L. Lobo, Igor Ballesteros, Izaskun Oregi, Javier Del Ser, and Sancho Salcedo-Sanz present an interesting work where stream learning is applied to a real use case in a combined cycle power plant.

Summary: The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power production as a function of these environmental conditions, in order to maximize the profit. The research community has solved this problem by applying Machine Learning techniques, and has managed to reduce the computational and time costs in comparison with the traditional thermodynamical analysis. Until now, this challenge has been tackled from a batch learning perspective, in which data is assumed to be at rest, and where models do not continuously integrate new information into already constructed models. We present an approach closer to the Big Data and Internet of Things paradigms, in which data are continuously arriving and where models learn incrementally, achieving significant enhancements in terms of data processing (time, memory and computational costs), and obtaining competitive performances. This work compares and examines the hourly electrical power prediction of several streaming regressors, and discusses about the best technique in terms of time processing and predictive performance to be applied on this streaming scenario.

Key words: electrical power prediction; combined cycle power plant; stream learning; online regression

Open Access: https://www.mdpi.com/1996-1073/13/3/740/htm

DOI: https://www.mdpi.com/1996-1073/13/3/740/htm

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