Table of Contents
This case study applies to building zones equipped with temperature and activity sensors, a controllable heating, ventilation and air conditioning (HVAC) system and, optionally, a local PV system.
Buildings are responsible for almost a third of global energy consumption and CO2 emissions. Their role in mitigating climate change is therefore crucial. HVAC systems alone account for around 40% of a building’s total energy consumption.
In this case study, we show that it is possible to reduce the consumption of HVAC systems by finding the optimal trade-off between minimising energy consumption, ensuring a good level of comfort, reducing energy costs and maximising the use of renewable energy.
As part of the Innoviris TeamUp research programme, Energis developed and deployed a solution consisting of a rule-based AI system capable of controlling HVAC devices in an intelligent and autonomous manner.
The solution was installed on our edge device Raspicy and tested in a scenario consisting of an office zone equipped with temperature, humidity, CO2, motion sensors and a local PV installation. Communication between the intelligent controller and the HVAC system was via the ModBus protocol, although several other alternatives can be considered, such as BMS and an existing HTTP API.
The rule-based AI controller has an inference engine that applies condition-action rules stored in the knowledge base to make decisions and infer new facts.
As a first step, a Knowledge Engineer collects (building) domain knowledge with the help of domain experts and then converts this knowledge into rules that can be added to the system’s knowledge base.
After this initial phase, the system works autonomously, interacting with the environment to perceive its state (what is my world like now?) and applying the rules stored in the knowledge base to decide the best next action (what should I do now based on what I know?).
As well as using sensor data, the rule-based controller uses weather and occupancy forecasts to proactively adapt to predicted environmental conditions. For example, preheat the zone if occupancy and low outside temperatures are expected in the coming hours.
As demonstrated during our research programme, our rule-based controller was able to improve the comfort level in the zone while reducing the energy consumption of the HVAC system without any human intervention. The optimisation algorithm also took into account renewable energy production, making maximum use of solar radiation whenever available.
In addition, the ability to predict weather and occupancy conditions was an important factor in increasing energy efficiency, e.g. by exploiting the thermal inertia of the building at the end of the working day.
Monitoring of the Controller
The two panels of the controller dashboard below were used to monitor the indoor temperature and the actions taken. The controller will preheat the office if it is cold and will be occupied in the coming hours. It made best use of the PV production when the irradiation was good enough and stopped heating when it was time to use the thermal inertia of the building until the end of the working day.
The comfort panels allowed us to monitor comfort in detail, for example, to follow the evolution of the Predicted Percentage of Dissatisfied (PPD) and Predicted Mean Vote (PMV) indices used by ASHRAE¹ to assess thermal comfort. PPD, shown as a green line in the comfort panel, estimates the percentage of people who are dissatisfied with the current level of comfort (cooling or heating) and must be less than 20% during working hours (grey area in the dashboards).
¹ The American Society of Heating, Refrigerating and Air-Conditioning Engineers. www.ashrae.org
Monitoring of KPIs
A global view of comfort conditions was also required. We created the dashboards below, which monitor comfort scores such as temperature and humidity. These scores indicate the percentage of time the indoor conditions were within the comfort range during working hours.
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