The integrity and efficiency of modern energy grids are increasingly reliant on accurate anomaly detection within energy consumption data. However, class imbalance poses significant challenges, where normal consumption patterns vastly outnumber critical anomalies, leading to biased detection models. This paper presents a novel heterogeneous deep ensemble model specifically designed to handle class imbalance in energy consumption anomaly detection. The architecture strategically integrates Bidirectional Long Short-Term Memory (BiLSTM) networks for capturing temporal dependencies and Convolutional Neural Networks (CNNs) for feature extraction. Cost-sensitive learning was incorporated to address class imbalance, with rigorous hyperparameter tuning using Bayesian optimization. The model was evaluated using the State Grid Corporation of China (SGCC) dataset containing 42,372 customers' electricity consumption data.The deep ensemble model achieved impressive performance metrics: accuracy of 97.5%, precision of 97%, recall of 99%, F1-score of 98%, and AUC-ROC score of 99%. Statistical analysis confirmed significant improvements over baseline methods (BiLSTM and CNN) and existing ensemble models, with p-values consistently below 0.05.The heterogeneous ensemble architecture demonstrates superior performance compared to individual models and existing approaches. Cost-sensitive learning effectively addresses class imbalance while maintaining high accuracy. The findings establish new performance benchmarks for anomaly detection in energy systems with significant implications for energy efficiency, grid stability, and infrastructure security.