Case Study

Structure Dependent Weather Normalization Algorithm to Normalize and Forecast Energy Footprints

Written by: Gary Michor (CEO), Screaming Power Inc.

Structure Dependent Weather Normalization Algorithm to Normalize and Forecast Energy Footprints

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Screaming Power announces its research on a weather normalization method for “Structure Dependent Weather Normalization”. It is now published in the Energy Science & Engineering Journal. This innovative analytical method was developed in cooperation with Ryerson University and Ontario Center of Excellence to normalize and forecast the energy usage/loss of residential and commercial buildings while taking into consideration temperature, humidity, solar radiation and wind. This machine-learning/deep-learning process is more resilient to today’s extreme weather conditions than existing methods used by the energy market. 

It’s obvious that weather conditions have a substantial effect on energy and economic activity. Its also well documented that today’s climate changing activities makes simple normalization analysis a challenge, as existing relatively simple mathematical formulas become compromised when they have to adjust to more adhoc environmental changes. Adaptable weather normalization is an important step in building energy ratings, comparing buildings and greening generation, retrofit and storage management. Accounting for the impacts of weather on building energy use is an extremely exhaustive challenge because of the complexity and diversity in the operation of the mechanical and electrical systems, as well as the energy savings innovations being added to the building itself.  

In traditional weather normalization methods, building parameters, such as building size, window size, construction joints, and the effect of flues, are missing. The Ryerson researcher’s paper presents a Structure Dependent Energy Usage/Loss model by utilizing artificial intelligence algorithms to capture and forecast the behavior of energy consumption/loss. It is the basis of the Research that continues today at Screaming Power, where we are breaking barriers in managing energy use and the building footprint for today’s ever-changing global climate change dilemma. 

The published paper can be found at Energy Science & Engineering Journal via  Screaming Power’s continued work on this topic will be presented in the coming months after 2 years of research on weather normalization and Cognitive Conservation.  This innovative intelligence algorithm and method is patent pending and applied in Screaming Power’s business tools for Energy Utilities and large enterprise customers wishing to discover better ways to manage, analyze and forecast energy costs today and in the future.  

Screaming Power wants to thank Dr. Soosan Beheshti, Dr. Asad Sahebalam and Edward Nidoy for their hard work and commitment to helping make energy management more accurate in the changing weather landscape.  

For more information on this paper and continued work, contact Screaming Power at  

About Screaming Power Inc.

Screaming Power is revolutionizing customer engagement by providing a mobile platform that connects the energy user, allowing for effective and secure two-way communications to educate, change behaviour and encourage sustainability. Our extensible Intellectual Property provides a low-cost, digital infrastructure for a self-sustaining Eco-System. Our Scream Utility & Scream Enterprise mobile solutions focus on reducing ‘cost-to-service’ for utilities while driving satisfaction and facilitating the delivery of innovation (e.g., connectivity to the IoTs). 

About Ryerson

Ryerson University is Canada’s leader in innovative, career-oriented education. Urban, culturally diverse and inclusive, the university is home to more than 45,300 students, including 2,600 Master’s and PhD students, 3,800 faculty and staff, and nearly 198,000 alumni worldwide For more information visit ryerson website.

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