Abstract
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.
Highlights
The world’s level of urbanization is expected to increase from about 55% in 2018 to68% in 2050, and 90% of this increment is expected to take place in Asia and Africa, which were home to about 90% of the world’s rural population in 2018 [1]
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
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 on both fourth and fifth generation district heating and cooling networks
Summary
The world’s level of urbanization is expected to increase from about 55% in 2018 to68% in 2050, and 90% of this increment is expected to take place in Asia and Africa, which were home to about 90% of the world’s rural population in 2018 [1]. The world’s level of urbanization is expected to increase from about 55% in 2018 to. In urban areas where the heating and cooling demand exhibits the highest density and the largest load simultaneity, a huge amount of low-grade excess heat is wasted. Six out of ten of the top European heatwaves between 1950 and 2014 have appeared in the latest 20 years [3] Extreme weather events, such as wind storms and flooding, have increased in number and intensity worldwide [4]. 2019 has been defined the year of the “climate emergency” declaration In this context, the European Union, which already demonstrated between 1990–2018 that it is possible to decouple gross domestic product (GDP) growth from greenhouse gas emissions [7], set a very ambitious target to achieve carbon neutrality by 2050
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