 This paper proposes an intelligent data mining model to analyze, forecast and visualize energy time series to uncover various temporal energy consumption patterns. The model uses unsupervised data, clustering and frequent pattern mining analysis to identify appliance usage patterns from concurrent streams of data. Additionally, it performs Bayesian network prediction for energy usage forecasting. Experiments were conducted using real-world context-rich smart meter datasets, and the results showed that the proposed model outperforms other models at each stage while achieving combined accuracy of 81.82%, 85.9%, 89.58% for 25%, 50% and 75% of the training data size, respectively. Furthermore, the model was able to achieve energy consumption forecast accuracies of 81.89% for short-term, hourly, 75.88%, 79.23%, 74.74%, and 72.81% for day, week, month, and season, respectively. This article was authored by Shalendra Singh and Abdul Salam Yaseem.