A data-driven analytic approach for investigation of electricity demand variability for energy conservation programs

Targeting and designing effective demand response and energy efficiency strategies is a challenging task owing to the variations in electricity usage patterns. To address this problem, we attempt to propose a clustering and Kullback-Leibler (KL) Divergence-based solution to understand the variability across the different consumption patterns. The proposed unsupervised approach utilized hierarchical clustering technique for segmenting the customers based on the similarity in their consumption behaviour, followed by the incorporation of the KL divergence to quantify the variability for different features of customer's load profiles. These features aid in understanding the diversity in load shapes. Depending on the different features, the results reveal that a few of the consumers having certain distinctive consumption can be easily considered as potential for demand flexibility, while others require careful consideration for program offerings. The result also shows significantly higher day-to-day variations in the clustered groups and can be managed by designing suitable behavioural modification strategies. The variability assessment of individual customer profiles helps better comprehend the diversity in usage behaviour. It leads to informed decision-making in planning demand response and energy efficiency initiatives, which was not taken into consideration in the previous studies.