Electrical Load Forecasting with Anomaly Detection and Explainability

Abstract

Managing electricity efficiently has become a crucial part of modern living. Power usage constantly changes based on time, weather, and user behavior, making it difficult to predict and manage. Traditional forecasting methods like ARIMA or LSTM can predict trends but fail to explain why certain fluctuations occur or when abnormal consumption happens. This project — Electricity Load Forecasting with Anomaly Detection and Explainability — focuses on predicting energy consumption accurately while also detecting unusual usage patterns (called anomalies) and explaining the reasons behind them. The system uses a Transformer-based forecasting model to predict future electricity load, a statistical anomaly detector to find abnormal spikes or drops, and SHAP (SHapley Additive Explanations) to explain which features influence the results. A simple Streamlit dashboard displays real-time forecasting, anomaly visualization, and feature importance charts. This makes it easy for users and energy operators to understand power trends, reduce wastage, and maintain a more stable and efficient energy system.

Introduction

Rapid urbanization, industrialization, and digitalization have significantly increased energy consumption, making accurate electricity load forecasting essential for power system planning, fault detection, and cost optimization. Traditional statistical models like ARIMA struggle with nonlinear load variations caused by dynamic user behavior, weather, and special events. Machine learning and deep learning approaches, including LSTM and GRU, improve accuracy but are computationally heavy and less interpretable.

This project addresses these limitations using a hybrid framework that integrates three techniques:

1. Transformer-based forecasting – efficiently captures long-range temporal dependencies in electricity load data.

2. Anomaly detection – identifies unusual consumption spikes or drops using the Mean + 2×Standard Deviation method.

3. Explainable AI (SHAP) – interprets the impact of features such as temperature, hour, and humidity on predictions.

The methodology involves:

• Data collection from historical electricity consumption and external factors (weather, calendar data).

• Preprocessing to clean, normalize, and structure data, handling missing values and outliers.

• Feature engineering to create informative features like lag values, moving averages, and day-type indicators.

• Model training using a Transformer to forecast future load while capturing temporal patterns efficiently.

• Anomaly detection to flag unexpected load variations.

• Explainability through SHAP, providing insight into how each feature affects forecasts.

• Visualization on a Streamlit dashboard showing forecast trends, anomalies, and feature contributions.

The literature shows that deep learning models (ConvLSTM, Temporal Fusion Transformer, hybrid architectures) achieve high accuracy but often require heavy computation and are difficult to interpret. Transformer-based models, combined with explainable AI and anomaly detection, provide a balanced solution that is accurate, interpretable, and suitable for real-time smart grid applications.

Conclusion

The Forecasting of Electricity Load system effectively demonstrates the effectiveness and stability of applying a Transformer-based architecture combined with anomaly detection and explainable AI for modern energy analytics. The model not only delivers high forecasting accuracy but also ensures interpretability and computational efficiency, addressing key limitations of conventional statistical methods and deep learning models. By incorporating robust feature engineering—such as lag values, moving averages, weather-related parameters, and calendar information—the system is able to capture both short-term fluctuations and long-term trends in electricity consumption. The anomaly detection module enables the identification of abnormal load patterns, which may indicate equipment faults, sudden consumption spikes, or meter errors, thereby enhancing operational monitoring and grid reliability. Real-time data retrieval from smart meters or open-source datasets ensures that the system remains flexible and adaptive to changing consumption patterns. SHAP-based explainability provides transparent insights into the contribution of each feature toward the predicted load, bridging the gap between predictive performance and interpretability. The evaluation metrics, including Mean Squared Error (MSE) and visual comparisons of predicted versus actual loads, validate the reliability, stability, and robustness of the proposed model. This system can be further extended by integrating renewable energy parameters, hybrid modeling techniques, and cloud-based dashboards for scalable real-time deployment. In conclusion, the project establishes a usable, interpretable, and real-time electricity load forecasting framework that supports intelligent decision-making, grid optimization, and anomaly detection in modern smart grid environments.

References

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Copyright

Copyright © 2025 S. Suganthi Shyam, S. Reshmaa, J. Jephita Janish. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.