Hyperlocal Weather Insights to Improve Energy Efficiency

A key to meeting increased energy demand and improved carbon neutrality is using precise, real-time local weather data and modeling to optimize energy networks.


https://facilityexecutive.com/2021/11/hyperlocal-weather-insights-to-improve-energy-efficiency/
A key to meeting increased energy demand and improved carbon neutrality is using precise, real-time local weather data and modeling to optimize energy networks.

Hyperlocal Weather Insights To Improve Energy Efficiency

Hyperlocal Weather Insights to Improve Energy Efficiency

By Hannamari Jaakkola

As the world moves further into the digital age, forward-thinking cities are increasingly leveraging smart technologies, data, and partnerships to become safer, healthier, and more efficient. In just the past few years, leaders in cities around the globe have begun utilizing the deluge of data at their disposal to tackle many of the challenges urban life poses, from preparing for severe weather events and air pollution mitigation to traffic optimization, street network maintenance, and much more.

WeatherToday, utilities are tasked with meeting increasing energy demand and improving carbon neutrality, businesses are aiming to optimize energy use, and both utilities and businesses are searching for new ways to efficiently meet the challenges without increasing costs. The key to meeting these challenges is using precise, real-time local weather data and modeling to optimize energy networks.

When it comes to facility management, the environment outside has a significant impact on what’s happening inside, which is why facility management executives are focusing on microclimate measurements outside their facility to optimize the use of their HVAC systems, and ensure the efficiency of their facilities while managing the comfort of their occupants.

Impact Of Weather On Energy Use

With big data technologies beginning to transform the way smart cities operate, accurate and reliable weather information can do much more for smart cities than simply predict the weather: The data high-quality weather sensors collect can be used to improve living and working conditions as well as the environment.

Let’s take a look at the impact that weather has on energy efficiency, including district heating, using Espoo, the second-largest city in Finland, as an example.

But first, what is district heating? A centralized method of distribution, district heating provides thermal energy to multiple buildings from a central energy plant, or plants, through highly insulated underground thermal piping networks. By transferring thermal energy to a building’s heating system for both space and water heating, the need for boilers in individual buildings is avoided.

In Espoo, district heating currently contributes more than half of the city’s carbon emissions. With the city committing to carbon-neutral district heating within the next few years, city leaders are working with Fortum, a leading clean-energy company, to replace fossil fuels with smart and flexible sources, such as waste heat, renewable electricity, geothermal energy, and bioenergy.

With the weather being the biggest factor influencing energy demand, it’s important that decision-makers understand the microclimates in different parts of the city because varying conditions can have a massive impact on the ability to gain energy efficiency. With temperature being the most important factor, any changes in wind, precipitation, and the frequency and severity of extreme weather events will impact how much energy is produced, delivered, and consumed.

Microclimate Monitoring, Actionable Data Insights

Temperature can be very different within a city due to microclimates—even in an area as little as 200 meters there can be variances of several degrees—and forecasts are more accurate for weather station locations. Consequently, a dense weather station network enables more accurate forecasting of microclimates.

weatherTo better understand the microclimates in Espoo, Fortum installed six new weather stations to provide hyperlocal weather data across the entire network. By installing these new weather stations, Fortum is able to accurately forecast local heat demand in different neighborhoods, then plan the corresponding heat sourcing and distribution. Taking this proactive measure allows Fortum to improve the region’s energy efficiency, reduce emissions, and increase the security of the energy supply.

Employing the same approach can help facility management executives save energy, improve the indoor environment, and reduce climate impact. To reap the full benefits of microclimate monitoring, facility managers will need both:

  • A customized hyperlocal approach in monitoring and collecting accurate weather data.
  • Machine learning-enabled forecasting within the system on a micro-level.

A hyperlocal forecast is generated by combining different forecasts with local data and utilizing observations from various weather stations and measurement devices. Through machine learning, forecast systems can improve the forecast accuracy close to a weather station. Machine learning systems compare forecasts with accurate readings from weather stations to deliver more accurate hyperlocal forecasts.

Hyperlocal weather information empowers decision-makers to boost the energy efficiency of properties, raise operational accuracy through automation and optimization, transition to clean energy, efficiently recycle excess energy, and increase flexibility in distributed energy resources.

Optimizing HVAC Systems

With the vast majority of building-related carbon emissions coming from energy use for heating, cooling, ventilation, and lighting, reducing energy consumption through more efficient day-to-day building operations and optimal control of HVAC systems can lower CO2 emissions, increase energy efficiency, and reduce energy costs.

Since there is a direct relationship between a building’s day-to-day operations, energy needs, and weather conditions, microclimate measurements empower facility management executives to optimize the use of their HVAC systems and ensure the efficiency of their facilities by foreseeing changes in the weather before they occur. Model-predictive control uses real-time building weather and local forecast data, as well as occupancy and use information, to control heating, cooling, ventilation, and air conditioning, essentially anticipating the energy needs of the building and optimizing its thermal behavior.

As cities and buildings continue to get smarter, the potential to align a building’s operational energy behavior with that of the local weather conditions in the microclimate outside has never been greater. Short-term advantages include the ability to save money on heating and cooling, while the longer-term advantages enable buildings to become more resilient and sustainable. Accurate and reliable weather information and local forecast data can help facility management executives meet the energy usage demands of today—and prepare for changes in the future.

weather

 

Jaakkola is a senior market manager for Urban Weather and Environment solutions at Vaisala, a global leader in weather, environmental, and industrial measurement.

Click here for more facility management news related to energy efficiency.

Suggested Links:

LEAVE A REPLY