Adaptive Control of House Environment- Neural Network House

Presented by Wenjie Zeng

The Main Idea – Why Adaptive Control ?

• Smart houses aren’t smart

– Difficult to customize

– Complex interface baffles inhabitants

– Inhabitants just forget to start the “automation”

• Adaptive house

– Based on inhabitants’ lifestyle

– Self-configuration, learning through mistakes

– Change itself when inhabitants’ lifestyle changes

The Main Achievements – Optimal Control

• What did this work accomplish?

– Air and water temperature regulation

– Lighting (e.g. sleeping, reading, watching TV)

– Neural nodes communication and coordination

• What are the contributions?

– A mechanism to anticipate inhabitants’ needs

– Saving energy (water not fixed at a temperature)

– Optimal Control Model : Cost = Discomfort + Energy

The Challenges – How to predict

• Is inhabitants’ life pattern sufficiently regular ?

• Butterfly effect

– Wrong decision -> future state - > future decision -> …

• Inhabitants have to get through the learning

process of the adaptive system

• Example : Occupancy Model

Pictures

• Actual Deployment

Innovation

• Real Smart - Minimum human interaction

• Self-configuration through time

• Optimal Control method

– event-based decision making