Abstract
Demand for affordable and sustainable energy is growing. Even though the technology of construction and insulation of buildings is continuously improving, heating is still a significant issue for large part of Europe. Building modern heating systems as well as upgrading existing ones requires incorporating new technology and smart control systems with sophisticated control algorithms. An essential part of the control systems are models that allow the simulation to verify proposed actions or use series of simulation experiments to find the optimal solution. Several simulation tools are specializing in the field of energy already, and some general tools can also be used. This article shows two methods of own prediction mechanism of the heat demand of individual consumers (buildings). Modelling of individual buildings is the basis of the simulation model of district heating which is being developed. The fundamental idea is to build a modular model for specific district heating and start from the endpoints - from the individual consumption objects that will be interconnected through the distribution model with other parts of district heating system such as other consumers and producers. It is assumed that the heat demand is the most challenging part of the prediction, and therefore the accuracy and quality of these models will be the most significant to the accuracy of the entire future result.
Highlights
District heating and cooling systems (DHCS) are relatively large energy consumers, especially in countries geographically located in areas where colder periods occur when the outside temperature drops to values that are difficult for humans without heating to live in or it is not at all possible
In the context of the current situation where the requirements for energy consumption in general are rising, and with the prospect that classical resources are limited, the solution is, on the one hand, to use conventional resources as efficiently as possible, and on the other hand use more alternative resources, especially renewable. These requirements affect DHCS by: - to use the energy produced most efficiently, i.e., to minimize its losses, - by changing the structure of the DHCS to achieve more significant use of alternative sources. This requirements dramatically affects the management of the entire DHCS, and the inclusion of these new trends leads to new approaches and the use of modern methods for creation and use of DHC control system [1]
Heat demand modeling and the ability to predict it is significant for effective planning and optimization of heat production and distribution
Summary
District heating and cooling systems (DHCS) are relatively large energy consumers, especially in countries geographically located in areas where colder periods occur when the outside temperature drops to values that are difficult for humans without heating to live in or it is not at all possible. In the context of the current situation where the requirements for energy consumption in general are rising, and with the prospect that classical resources are limited, the solution is, on the one hand, to use conventional resources as efficiently as possible, and on the other hand use more alternative resources, especially renewable. These requirements affect DHCS by: - to use the energy produced most efficiently, i.e., to minimize its losses, - by changing the structure of the DHCS to achieve more significant use of alternative sources. Another large class of methods are methods using the machine learning technique to identify patterns in the heat demand
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