District heating companies are responsible for delivering the heat produced in central heat plants to the consumers through a pipeline system. At the same time they are expected to keep the total heat production cost as low as possible. Therefore, there is a growing need to optimise heat production through better prediction of customers needs. The paper illustrates the way neural networks, namely self-organised maps can be used to investigate long-term demand profiles of consumers. Real-life historical sales data is used to establish a number of typical demand profiles
To run a district heating system as efficiently as possible correct unit-commitmentdecisions has to ...
[EN] An accurate characterization and prediction of heat loads in buildings connected to a District ...
The use of smart energy meters enables the monitoring of large quantity of data related to heat cons...
Abstract: The advent of modern low-cost monitoring and wire-less transmission systems results in unp...
The advent of modern low-cost monitoring and wireless transmission systems results in unprecedented ...
The electricity grid is currently transforming and becoming more and more decentralised. Green energ...
Demand for affordable and sustainable energy is growing. Even though the technology of construction ...
By combining a cluster of microCHP appliances, a virtual power plant can be formed. To use such a vi...
With the building sector standing for a major part of the world's energy usage it of utmost importan...
In the recent years machine learning algorithms have developed further and various applications are ...
Recent development in the energy sector in Norway has made district heating increasingly relevant. I...
Fluctuating power production in combined heat and power (CHP) plants may cause unwanted disturbances...
A reliable preliminary forecast of heating energy demand of a building by using a detailed dynamic s...
A large proportion of the energy consumed by private households is used for space heating and domest...
Meeting the goal of zero emissions in the energy sector by 2050 requires accurate prediction of ener...
To run a district heating system as efficiently as possible correct unit-commitmentdecisions has to ...
[EN] An accurate characterization and prediction of heat loads in buildings connected to a District ...
The use of smart energy meters enables the monitoring of large quantity of data related to heat cons...
Abstract: The advent of modern low-cost monitoring and wire-less transmission systems results in unp...
The advent of modern low-cost monitoring and wireless transmission systems results in unprecedented ...
The electricity grid is currently transforming and becoming more and more decentralised. Green energ...
Demand for affordable and sustainable energy is growing. Even though the technology of construction ...
By combining a cluster of microCHP appliances, a virtual power plant can be formed. To use such a vi...
With the building sector standing for a major part of the world's energy usage it of utmost importan...
In the recent years machine learning algorithms have developed further and various applications are ...
Recent development in the energy sector in Norway has made district heating increasingly relevant. I...
Fluctuating power production in combined heat and power (CHP) plants may cause unwanted disturbances...
A reliable preliminary forecast of heating energy demand of a building by using a detailed dynamic s...
A large proportion of the energy consumed by private households is used for space heating and domest...
Meeting the goal of zero emissions in the energy sector by 2050 requires accurate prediction of ener...
To run a district heating system as efficiently as possible correct unit-commitmentdecisions has to ...
[EN] An accurate characterization and prediction of heat loads in buildings connected to a District ...
The use of smart energy meters enables the monitoring of large quantity of data related to heat cons...