# Real-time Electrical Power Prediction in a Combined Cycle Power Plant

## TL;DR

This paper introduces an incremental learning approach for real-time electrical power prediction in combined cycle power plants, leveraging streaming data to improve efficiency and performance over traditional batch methods.

## Contribution

It presents a novel incremental learning framework for continuous power prediction, addressing the limitations of batch models in dynamic, real-time environments.

## Key findings

- Streaming regressors outperform batch models in processing time.
- Incremental models adapt better to changing environmental conditions.
- Proposed approach reduces computational costs significantly.

## Abstract

The prediction of electrical power in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power output can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power output 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 is arriving continuously 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 performance to be applied on this streaming scenario.