More and more sensors are used in industrial systems (machines , plants, factories...) to capture energy consumption. All these sensors produce time series data. Abnormal behaviours leading to over-consumption can be detected by experts and represented by sub-sequences in time series, which are patterns. Predictive time series rules are used to detect new occurrences of these patterns as soon as possible. Standard rule discovery algorithms discretize the time series to perform symbolic rule discovery. The discretization requires fine tuning (dilemma between accuracy and understandability of the rules). The first promising proposal of rule discovery algorithm was proposed by Shokoohi et al, which extracts predictive rules from non-discretized data. An important feature of this algorithm is the distance used to compare two sub-sequences in a time series. Shokoohi et al. propose to use the Euclidean distance to search candidate rules occurrences. However this distance is not adapted for energy consumption data because occurrences of patterns should have different duration. We propose to use more " elastic " distance measures. In this paper we will compare the detection performance of predictive rules based on several variations of Dynamic Time Warping (DTW) and show the superiority of subsequenceDTW.