Energy Time Series Analysis

Introduction

Our AI-Powered Energy Analytics Platform leverages cutting-edge machine learning techniques to provide comprehensive energy data analysis services. The platform addresses critical challenges in energy management by offering accurate consumption forecasting, production optimization, appliance-level monitoring, and anomaly detection. This solution empowers utilities, businesses, and researchers to make data-driven decisions for improved energy efficiency and grid stability.

Key Features

• Short-term Consumption Forecasting: High-precision energy demand prediction for operational planning and load balancing
• Long-term Consumption Analysis: Strategic energy planning with extended forecasting horizons for capacity planning
• Solar Production Forecasting: Weather-based renewable energy generation prediction using advanced transformer based modeling
• Non-Intrusive Load Monitoring (NILM): Individual appliance energy consumption breakdown using BERT4NILM architecture
• Anomaly Detection: Real-time identification of unusual energy patterns using CNN autoencoder technology

Technologies Used

• Deep Learning: PatchTST (Patch Time Series Transformer) for advanced time series forecasting of consumption (A Time Series is Worth 64 Words), MatNet for fusing predicting day-ahead pv production MATNet: Multi-Level Fusion Transformer-Based Model for Day-Ahead PV Generation Forecasting) BERT4NILM transformer architecture for appliance disaggregation (BERT4NILM: A Bidirectional Transformer Model for Non-Intrusive Load Monitoring) and CNN autoencoders for anomaly detection

Use Cases

Local Energy Communities: Optimize energy sharing within neighborhoods by predicting when community solar panels will generate excess power and when local demand will peak. Help communities balance local production and consumption to reduce grid dependency.

Community Solar Projects: Forecast solar generation for shared community installations to help residents plan energy usage and maximize self-consumption. Enable fair energy allocation among community members based on predicted production.

Household Energy Insights: Use non-intrusive load monitoring to break down each home’s total energy consumption into individual appliances without installing separate meters. Help community members understand their energy usage patterns and identify opportunities for efficiency improvements.

Community Resilience Planning: Detect energy anomalies across the local network that could indicate equipment issues or unusual consumption patterns, helping communities maintain reliable energy supply and prevent outages.

Live Demo

Input

The platform accepts JSON-formatted energy data across all models, with the dashboard providing sample input examples:

• Time Series Data: Historical energy consumption/production data in JSON format
• Weather Data: Solar irradiance, temperature, humidity for production forecasting (JSON)

How it works

The solution employs a multi-model approach: Short-term and long-term forecasting uses PatchTST (Patch Time Series Transformer) architecture that segments time series into patches for improved pattern recognition and forecasting accuracy. Solar production forecasting integrates weather forecasting APIs with the same MATNet framework optimized for renewable energy generation patterns. NILM leverages the BERT4NILM transformer to identify appliance signatures from aggregate consumption data. Anomaly detection uses CNN autoencoders trained on normal consumption patterns to identify deviations in real-time.

Output

• Consumption Forecasts: day-ahead to week-ahead predictions
• Production Estimates: day-ahead Solar generation forecasts with weather-based accuracy metrics
• Appliance Breakdown: Individual device consumption estimates and usage patterns
• Anomaly Alerts: Real-time notifications with severity scores and probable causes

Try it out

Try out the solution in real time on Hugging Face Spaces:

👉 Launch Demo