Accurate electricity demand forecasting is crucial for grid stability and resource optimization, yet predictive models degrade over time due to data drift caused by market fluctuations, policy changes, and infrastructure shifts. Undetected drift leads to forecasting errors, inefficiencies in energy distribution, and increased operational costs. This paper presents a deep learning-based framework for detecting data drift in electric load time series, leveraging statistical and machine learning-based approaches with a focus on the Pruned Exact Linear Time (PELT) algorithm for efficient change-point detection. Our method is scalable and highly efficient, making it suitable for real-time applications. Unlike traditional drift detection techniques, our approach dynamically adjusts to evolving patterns, mitigating both gradual and abrupt changes in consumption behavior. By leveraging both synthetically generated multivariate data and realworld univariate data for electricity load-related time series, the effectiveness of PELT is evaluated demonstrating its ability to capture structural shifts with high accuracy. The proposed framework achieved F1 score of 82% and 98% for generated temperature and humidity time series, respectively. Similarly, it achieved F1 score of 94% for real-world electricity demand data. We also assessed the quality of PELT’s detection by applying time series smoothing techniques that provided additional insights.