Journal
APPLIED SCIENCES-BASEL
Volume 11, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/app11010455
Keywords
model predictive control; mixed-integer linear programming; load forecast; predictive maintenance; 4GDH; 5GDHC
Categories
Funding
- European Union's Horizon 2020 research and innovation programme, Renewable and Waste Heat Recovery for Competitive District Heating and Cooling Networks-REWARDHeat [857811]
- H2020 Societal Challenges Programme [857811] Funding Source: H2020 Societal Challenges Programme
Ask authors/readers for more resources
This paper reviews the main objectives of advanced district heating and cooling systems, as well as the challenges and innovative methods developed by researchers to address them. It discusses the application of model predictive control and machine learning algorithms in meeting these objectives. The strengths and weaknesses of traditional approaches and innovative technologies are compared and analyzed.
Peak shaving, demand response, fast fault detection, emissions and costs reduction are some of the main objectives to meet in advanced district heating and cooling (DHC) systems. In order to enhance the operation of infrastructures, challenges such as supply temperature reduction and load uncertainty with the development of algorithms and technologies are growing. Therefore, traditional control strategies and diagnosis approaches cannot achieve these goals. Accordingly, to address these shortcomings, researchers have developed plenty of innovative methods based on their applications and features. The main purpose of this paper is to review recent publications that include both hard and soft computing implementations such as model predictive control and machine learning algorithms with applications also on both fourth and fifth generation district heating and cooling networks. After introducing traditional approaches, the innovative techniques, accomplished results and overview of the main strengths and weaknesses have been discussed together with a description of the main capabilities of some commercial platforms.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available