The value of data

Meter data is knowledge. Without detailed information about the actual state of the network and the energy flow in the system, heat utilities are unable to know which levers to tweak and how to evaluate their effect. In short, you cannot optimise what you do not measure – and as the frequency of data increases, so does your basis for optimisation and the value you can create.

The knowledge derived from frequent meter data has a wealth of possible uses that support heat utilities in their daily work. Below are some of the most relevant application areas – today and in the future.

• Revenue protection
• Improvement of customer service
• Identification of faulty or misadjusted substations
• Monitoring temperature levels in the distribution network
• Leakage detection
• Identification of heat and water loss
• Enhancing end-user involvement
• Modelling buildings
• Shaping peak demand

Frequent data ensures an always updated and accurate basis for billing and it allows utilities to be in full control of their revenue. For example, you are able to closely monitor the end users’ consumption to see if it develops as expected, which enables early fraud detection and identification of other irregularities. In addition, frequent data allows for continuous surveillance of the meters ensuring quick detection and correction of errors, e.g. a broken temperature sensor.

Smart metering promotes a more proactive dialogue with end users, as utilities can help them see the consequences of their energy behaviour and provide them with data-based advice on individual energy optimisation. This is a key element in maintaining district heating’s position as an attractive and competitive product.

Frequent data enable you to identify opportunities for improvement and to proactively contact the relevant end users to help them optimise their heat installation.
It is estimated that 75% of all substations can be improved in terms of efficiency and that the problems can be identified with hourly values from smart meters.

Gadd, H. (2014). To analyse measurements is to know!

Temperatures in the distribution network must be lowered in order to improve energy efficiency and to create the right circumstances for the integration of e.g. solar and heat pumps in the network. Frequent data provide you with online updated information about the actual temperatures in the network, which is the basis for determining the lowest acceptable level of the forward temperature to still provide a satisfactory service to your end users.

Some energy meters are able to measure both forward flow and return flow, which allows utilities to identify buildings where treated water from the distribution network is lost in buildings or substations. Monitoring and reducing the leakage level saves costs for adding and heating new treated water to the system and it enables you to detect installations where water intrusion into the district heating system is causing problems with the quality of the treated water.

By combining frequent data from the end user with information from strategic locations in the overall distribution network, utilities can identify the difference between the energy fed into the individual network zones compared to the heat that is actually consumed in the buildings. This allows you to continuously monitor the heat loss and quickly spot negative or positive trends. In strategic parts of the network, energy meters may be supplemented with other measurements, e.g. pressure, in order to provide even more detailed information from the network.

With more frequent data, utilities can offer end users additional services including online energy management services, or maybe even offer to operate the end user’s heat installation in the most energy efficient way. In addition, real-time meter data opens up to the possibility of introducing new future billing schemes that support a more energy efficient heat supply. Such schemes could perhaps be based on the end users’ degree of flexibility rather than their energy consumption allowing them to use more heat when surplus heat is available in the network and to rely on thermal storage during peak hours.

Predicting how buildings will behave under different circumstances will be a key tool for planning production, using the buildings for thermal storage or evaluating the need for renovation. Modelling buildings can be used for energy labelling and making suggestions for improvements based on their thermal profiles. Knowing that a building consistently performs poorly when cold winds blow from the west, or that it would be profitable to replace the windows so they can absorb more solar energy would enable you to take the necessary action to improve their energy performance.

To stay competitive and to run an optimal production it is often desirable to reduce demand peaks. Intelligent meters can be used for power limiting a heat installation forcing the end user to shape their demand peak. This can create the right circumstances for connecting more and more buildings to the existing district heating system, which is a very important topic in the new EU future where there will be a push for more district heating. The answer is not always simply to renew all pipes in the ground in order to improve capacity as this can increase the costs and make district heating less attractive.