 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 on energy time series and Bayesian network prediction for energy usage forecasting. The accuracy results of identifying appliance usage patterns using the proposed model outperform support vector machine SVM and multi-layer perceptron MOP at each stage while attaining a combined accuracy of 81.82%, 85.9%, 89.58% for 25%, 50% and 75% of the training data size respectively. Additionally, the model achieved energy consumption forecast accuracies of 81.89% for short-term, hourly, 75.88%, 79.23%, 74.74% and 72.81% for the day, week, month, and season respectively.