  In this chapter, we report on our experience with domestic flexible electric
energy demand based on a regular commercial (HVAC)-based heating system in a
house. Our focus is on investigating the predictability of the energy demand of
the heating system and of the thermal response when varying the heating system
settings. Being able to form such predictions is crucial for most flexible
demand algorithms. We will compare several methods for predicting the thermal
and energy response, which either gave good results or which are currently
promoted in the literature for controlling buildings. We will report that the
stochasticity of a house response is -- in our experience -- the main
difficulty in providing domestic flexible demand from heating. The experiments
were carried out on a regular house in Norway, equipped with four air-to-air
Mitsubishi heat pumps and a high-efficiency balanced ventilation system. The
house was equipped with multiple IoT-based climate sensors, real-time power
measurement, and the possibility to drive the HVAC system via the IoT. The
house is operating on the spot market (Nord Pool NO3) and is exposed to a peak
energy demand penalty. Over a period of three years, we have collected data on
the house (temperatures, humidity, air quality), real-time power and hourly
energy consumption, while applying various flexible demand algorithms
responding to the local energy costs. This has produced large variations in the
settings of the heating system and energy demand, resulting in rich data for
investigating the house response. This chapter aims at providing important
insights on providing flexible demand from houses in cold climates.
