Somewhere between 30 and 40% of the global energy consumption occurs in buildings (United Nations Environment Programme, 2007). Thus, buildings represent an important opportunity to reduce energy consumption, and further help mitigate global warming, one of the world's most important problems. Most of the energy in buildings is used to maintain air quality and the right temperature inside the buildings. This is controlled by cooling, heating and ventilation systems, known as the technical building system. When this system is not efficiently operated, more energy is required, which will incur higher energy cost, service costs and CO2 emissions. Additionally, a non-efficiently operated building system will have a lower expected lifetime. Using unique smart-meter energy data from more than 50 million hourly observations; the proposed project will demonstrate how automated statistical methods and big-data techniques can help building owners lower their energy consumption and reduce maintenance costs. To accomplish this the project will conduct research within three different areas. First, maintenance costs constitute a significant percentage of expenses in most buildings. A typical (reactive maintenance) strategy is "wait until something breaks". Using machine learning (predictive maintenance) we will demonstrate how to reduce maintenance cost, and increase lifetime of the technical building system. Second, peak load tariffs are steadily increasing. Using predictive analytics, we will show how forecasting energy usage can be used to shift energy consumption from the main power line to locally produced solar and battery storage during peaks hours. These methods can reduce total energy costs, and peak shaving / phase shift can increase the lifetime of the power grid. Third, and last, the project will demonstrate how to benchmark energy consumption using machine learning and mathematical programming.
Project leader: Petter Arnestad
Institution: OHMIA RETAIL AS