How data integration can propel district heating to an efficient and sustainable future

DBDH webinar: Preparing Utilities for a Data Driven Future

We started the new year by looking forward and diving into solutions designed for district heating utilities. This webinar is of interest for DH utilities, consultants, and others keen on learning more about new ways to make DH greener, more efficient, and more digital.

Program:


Sustainability is now a key concern for businesses worldwide. Governments and international organizations have set ambitious targets to reduce the impact of climate change wherever possible. The US has announced new plans to boost the use of clean energy and reduce carbon emissions in the country by 2030, and the EU recently announced similar plans. This has placed the spotlight firmly on utilities and how they will respond to these targets.

Utilities, in turn, have increased investment into modern methods of service delivery and have rapidly digitized their operations to focus on the use of data. district heating networks are a clear example of where data integration and analysis can deliver value. However, in comparison to the electricity sector, digitalization in heating and cooling networks has not taken off to the same extent yet.

The global district heating market has grown significantly in recent years and this growth is expected to continue, reaching a market value of over $180 billion by 2026. Here is how district heating networks can deliver value to customers and business partners by effectively extracting, consolidating, and analyzing operational data.

How data can help district heating networks plug existing information gaps and improve operational efficiency

As with all utility services, data plays an essential role in the daily operations of district heating networks. District heating grids are reliant on their ability to predict demand and generate the appropriate amount of heat to be distributed across a fixed service delivery area. This capability is significantly enhanced combining data sources such as heat meter customer usage data, BBR data from the centralized buildings registry, GIS data from heat grids and weather data that can help district heating companies to identify the most valuable energy-saving investments and make better fact-based decisions.

Data collected from a network of smart meters and smart building fixtures can enable utilities to predict demand spikes or lulls to optimize production and delivery. Weather and customer data for example, provides the basis for predictive analytics that can guide efficient heat generation systems that usually require hours to deliver heat to customers.

Why business leaders struggle to fully realize the benefits of data integration and analysis

Customers expect comfort in their home and service quality from their heat supplier and utilities must always be prepared to meet these growing demands from their customers. To do this, utilities consult past usage data to predict and generate enough heat to be distributed as customers require. However, the customer data used to predict this demand is not always accurate, potentially leading to an energy shortage, over capacity, client dissatisfaction, reputation damage, and even in some cases monetary fines. It is crucial for utilities to unlock the data in legacy system silos and integrate, harmonize and provision data across the enterprise and with other market participants quickly and accurately.

Despite this, a fifth of utility data is believed to be inaccurate. This poor quality can arise due to an aging system architecture, manual work processes, information silos, time lag from outdated data delivery systems, and poor data management processes. Utilities must pay attention to these processes to ensure they can rely on their automated and data driven processes that help improve operational efficiency.


How Utilihive helps district heating suppliers provision data to meet their operational and sustainability goals

High-quality operational data enables predictive analytics for district heating networks

Having in-depth knowledge of the customers and a better understanding of their heat use is important for effective district heating operation and management. However, properly segmenting customer groups and discovering their typical and atypical consumption patterns is a complex task, especially for district heating systems involving many customers with different characteristics. Data-driven methods using different Machine Learning techniques to automatically cluster various customers and their consumption behaviors are typical use cases for district heating utilities.

Consolidated information empowers businesses to make data-driven investment decisions in district heating networks

Despite the vast amounts of data generated by connected devices in a utility’s technology stack, most of this information is used to analyze individual devices or to facilitate customer billing. However, data can be a very effective whistle blower in our fight for a fossil-free future. As an example, building heat meters holds a lot of information that can be compared to see which buildings are not utilizing energy efficiently. Combining data can also offer better insights to identify exactly what temperature is required to service specific customer groups and establish low temperature network zones where distributed renewable energy sources can offer surplus heat. Finally, by combining GIS system data, utilities can effectively and accurately identify areas of heat loss and leakages, predict maintenance requirements that can prolong asset lifecycles and reduce maintenance costs.

Operational data allows customers to be aware of excess energy usage

Sustainability is a shared responsibility. Data can empower utilities to operate efficiently and reduce wastage. However, the role of the end customer is often ignored. A recent Gallup poll revealed that concern over global warming and climate change is steadily increasing and more Americans believe that global warming will pose a serious threat in the near future. Operational data can show end users the amount of energy they use when they heat their homes and can encourage them to use energy more responsibly.

As more utilities embrace smart home devices, meters, and other IoT devices, the amount of data generated will only increase. Utility leaders can choose to make the most of the opportunities that this treasure trove of data can provide with modern systems designed to consolidate and analyze operational data.

With fully integrated data systems, District Heating suppliers can find areas of inefficiency and resolve them quickly. Data can also provide utilities a better understanding of their clients heat use for more effective district heating operation and management. Especially for the consumption side, analyzing how customers consume heat helps utilities to implement new control strategies, personalize demand management for specific customer groups and empower consumers to optimize their behaviors.

To stimulate cross-sector (buildings, industry, and heat and power generation) and cross-service (heating and cooling) synergies that can contribute towards an immediate climate-positive effect, more collaboration is needed in the sector. Utilities working towards an integrated infrastructure as well as interoperability will greatly benefit from improved collaboration and higher integration in the sector, through, for example, standardized monitoring systems, data sharing protocols and collaborative platforms.


About Greenbird
Greenbird is the leading provider of big data integration technology for the utilities sector and industrial IoT. Greenbird's flagship solution, Utilihive, simplifies data integration and big energy data management to help utilities achieve sustainable growth and accelerate the energy transition. From its headquarters in Oslo, Norway, Greenbird enables digital transformation in the utilities sector and drives the energy revolution. Read more at Utilihive.

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