Markku J Virtanen
Aalto
D.Sc.(Tech), Helsinki University of Technology.
Professor of Practice (HVAC- technology) at Aalto University. Previously worked several years at VTT in different positions e.g. as a technology manager and research professor. Virtanen has worked 1.5 years as Principle Lecturer at South-Eastern Finland University of Applied Science. Since 1990 Virtanen's research career has been research management and leadership within the scope of energy efficient and smart building technologies and services in different organizations (including strategic planning, action planning, management/leadership, sales, preparation support, advising, information dissemination).
A majority of our lifespan is spent inside buildings. Consequently the built environment accounts for nearly 40% of world energy consumption and 30% of emissions. In spite of the significant investment in operating buildings, occupant dissatisfaction remains widespread. The drive towards energy efficient buildings needs to be occupant-centric, making buildings smarter. Digitalization, cheaper sensors and actuators, the Internet of Things, and cloud based services will revolutionize energy efficient buildings and initiate new disruptive innovations. These provide entrepreneurial opportunities for cloud based services and sensor technology, while challenging building services, HVAC equipment companies, and investors to be a part. Building service providers need to provide their clients not only a building, but also performance commitments: energy, indoor environment, and occupant comfort.
The future holds smart grids integrated with smart buildings, advent of prosumers and bidirectional energy flows, and demand response (DR) control to better match energy demands with production, effective utilization of renewable energy, and lower peak power demands. To this end, an investigation was carried out into the impact of different DR algorithms on the energy use, indoor climate, and occupant perception in a building of the Aalto University. The DR algorithms controlled water supply temperature to radiator heating systems and supply temperature of ventilation air, based on the future district heating price trends. Nine different algorithms were implemented with two interspersed “reference” weeks when default settings were operational.
To evaluate the impact of the algorithms, building performance was monitored at multiple levels. Inlet and outlet water temperature, heating energy consumption, and outdoor temperature were recorded at the building level. For the ventilating units, AHU supply air temperatures, and AHU supply and exhaust air flow rates were monitored. The indoor climate conditions were monitored using temperature and carbon dioxide sensors. In a portion of the rooms, occupancy sensors kept track of room usage. Considering the need of an occupant-centric approach, feedback on the indoor thermal conditions was gathered from the users – primarily students. They served as an additional “sensor” of the prevalent indoor conditions.
Each period implementing a different DR algorithm was represented as a separate week – Weeks 7 through 11. Considering week-wise occupant feedback for weeks with 20 or more responses, the different demand response algorithms did not cause much variation in occupant perception. During the DR control Week 10, satisfaction reached close to 70%, compared to about 50% during the reference week. DR weeks kept satisfaction levels similar to that of the reference week when controls were kept at their default levels. DR led to change in inlet water temperatures to the radiators between -5 and 5 °C from the default values. Hence some of the algorithms improved occupant appreciation while saving energy.
As an additional advantage and for future works, the developed platform is able to accommodate further sensors and data collection seamlessly. Thus, it can serve as a test bed for innovators, encouraging innovation and development of disruptive technology for HVAC systems in intelligent buildings.
