This paper presents a system for visualizing electricity consumptiondata along with the implementation of a pattern recognition approach for peakprediction. Various classification algorithms and machine learning techniques aretested and discussed; in particular, Support Vector Machine (SVM), GaussianMixture Model (GMM) and hierarchical classifiers. Most notably, the bestalgorithms are able to detect 70% of the peaks occurring within the next 24 hours.Also, various ways of correlating energy consumption are considered in the presentcontext. Finally, a few directions for future work are discussed
