Data-Driven Domestic Flexible Demand: Observations from experiments in cold climate

TL;DR

This study investigates the predictability of domestic flexible energy demand in cold climates, focusing on heating systems' thermal response variability and evaluating different prediction methods over three years of real-world data.

Contribution

It provides empirical insights into the challenges of predicting flexible demand from heating in cold climates, highlighting the stochastic nature of house responses and comparing various prediction approaches.

Findings

Stochastic house response is the main challenge in demand prediction.

Different prediction methods show varying levels of accuracy.

Rich data from real-world experiments reveals the complexity of flexible demand in cold climates.

Abstract

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.