Demand Flexibility Estimation Based on Habitual Behaviour and Motif Detection

Nowadays the demand for energy is becoming higher and higher, and as the share of power supply from renewable sources of energy (RES) begins to rise, exacerbating the problem of load balancing, the need for smart grid management is becoming more urgent. One of such is the demand response technique (DR), which allows operators to make a better distribution of power energy by reducing or shifting electricity usage, thereby improving the overall grid performance and simultaneously rewarding consumers, who play one of the most significant roles at DR. In order for the DR to operate properly, it is essential to know the demand flexibility of each consumer. This paper provides a new approach to determining residential demand flexibility by identifying daily habitual behaviour of each separate house, and observing flexibility motifs in aggregate residential electricity consumption. The proposed method uses both supervised and unsupervised machine learning methods and by combining them acquires the ability to adapt to any new environment. Several tests of this method have been carried out on various datasets, as well as its experimental application in real home installations. Tests were performed both on historical data and in conditions close to real time, with the ability to partially predict Flexibility.