An Overview of Recent AI Applications in Combined Heat and Power Systems

Ashkan Safari () and Arman Oshnoei ()
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Ashkan Safari: Mechanical, Automotive & Materials Engineering Department, University of Windsor, Windsor, ON N9B 3P4, Canada
Arman Oshnoei: Department of Energy, Aalborg University, 9220 Aalborg, Denmark

Energies, 2025, vol. 18, issue 11, 1-31

Abstract: Combined heat and power (CHP) systems are among the important components for enhancing energy efficiency and sustainability by simultaneously generating electricity and useful thermal energy, reducing waste and costs. Consequently, the effective control of these systems is considered important. To that end, this paper provides a comprehensive review of the intelligent methodologies applied to CHP systems, emphasizing their prevalence in the USA and Europe through statistical insights. It outlines the mathematical foundations of CHP systems, analyzing the advancements in intelligent control methods for optimal planning, economic dispatch, and cost minimization. Artificial Intelligence (AI) models, such as Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Random Forest, are described and applied to a simulated CHP system. The Key Performance Indicators (KPIs) derived from these models demonstrate their efficacy for optimizing CHP performance. This paper also highlights the impact of AI-driven models for enhancing CHP system efficiency, while identifying the challenges in AI-CHP integration and envisioning CHP systems as important components of future sustainable energy systems.

Keywords: CHP systems; intelligent control; artificial intelligence; energy efficiency; optimal planning; sustainability; energy ecosystem (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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