ML tools in energy forecasting and predictive maintenance
Our company in short
Granlund is strongly growing group of companies with more than 1 500 experts in Finland, Sweden and UK. Our lines of business are MEP design (Mechanical, Electrical, and Plumbing), property management services and software, energy, environmental and real estate sector consulting, construction management and supervision and building management. Our key goal is to make properties more functional and smarter and to improve human well-being in the built environment.
Our business problem and machine learning
Buildings are responsible for 40% of global energy consumption and are therefore an interesting target for optimizing energy use using machine learning. In addition, the productivity growth in construction has been low compared to other industries. Thus, there is much potential for improvement and machine learning offers methods of achieving this.
Our first machine learning application area in the project is building energy management in a large building portfolio. Buildings are a challenging sector for machine learning as every building is different. Thus, there is a need for effective methods to develop and manage a high number of ML models.
Our research and solutions
In the IML4E project, we have researched tools and methods for data profiling and model monitoring of a high number of ML models. Within data profiling, we have studied data quality dashboards that could be used in continuous data quality monitoring and searched for ML solutions to inspect the quality of energy consumption data.
The project involved
- creating the ML application for detecting and predicting anomalies in building energy
- building MLOps platform and a model monitoring dashboard to assess the performance, data quality, and infrastructure of training and prediction.
MLOps platform was built to manage large number of ML models that the created application requires. The model monitoring dashboard is customized to the use case/business perspective, as it tracks multiple models per building for each application, instead of just specific models and it also tracks data quality and infrastructure serving the application.
Future direction and business benefits
A possible future direction is to develop applications for other types of time-series data from buildings (such as submetering, sensors, etc.), and then to adapt the MLOps and model monitoring platform to suit more general models, since in the future we may have models that can work for any building, instead of specific models for each one. This might bring more customers and increase the value of our monitoring and digital services.
