Research & Development Innovation for Energy Efficiency
Vision & Mission
Our R&D team collaborates closely with our sales and development departments to transform customer needs into intelligent solutions.
We conduct research, experimentation, and prototyping to identify:
AI-powered data processing
We transform raw data into valuable insights through advanced preprocessing and feature engineering, ensuring accuracy and real-time performance for every energy analysis.
AI Models
Our platform relies on state-of-the-art AI models and Large Language Models (LLMs) to deliver precise forecasts, detect anomalies, and provide intelligent, human-readable energy insights.
Secure and scalable AWS infrastructure
Deployed on a secure and scalable AWS infrastructure, our AI ensures reliability, seamless updates, and continuous real-time performance for energy optimization at scale.
Research Philosophy & Methodology
We rely only on public, licensed, or customer-authorized data, never personal information. Our datasets combine real-world measurements from Wattnow devices with customer inputs such as occupancy or equipment details, enriched by contextual data like weather. This ethical approach ensures privacy, transparency, and compliance while maintaining accuracy and trust in every energy insight we deliver.
Application Areas & Achievements
Energy Anomaly Detection
We developed advanced frameworks for detecting energy consumption anomalies, accurately identifying irregular patterns with minimal false alarms to enhance smart energy monitoring automation and reliability. 📖 Comparative Analysis of Off-the-Shelf Methods for Energy Consumption Anomaly Detection, IEEE International Conference on Automation, Robotics and Applications (ICARA 2024)
📖 Toward Unsupervised Energy Consumption Anomaly Detection, IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2024)
Energy Consumption & Production Forecasting
We develop intelligent forecasting systems for predicting energy consumption of clients, buildings, and industrial machines, as well as photovoltaic (PV) energy production based on weather and environmental data. Our AI-driven frameworks enable accurate short- and long-term forecasts, supporting optimized energy management, load balancing, and strategic decision-making.
📖 Learning-Based Short-Term Energy Consumption Forecasting, IFIP AIAI Proceedings (2024)
📖 Performance Evaluation of AI-Driven Photovoltaic Output Forecasting, IEEE PES/IAS PowerAfrica Conference (2024)
Handling Missing Energy Data
Our research introduces a robust imputation strategy for reconstructing incomplete energy datasets. The approach ensures analytical continuity and reliability, surpassing conventional methods in restoring data quality for both consumption and production analysis.
📖 Missing Energy Data Imputation: Addressing Missing Completely at Random Mechanism, Pattern Recognition and Artificial Intelligence: Selected Papers from MCPRAI 2024 (Springer LNNS, 2025)
Renewable Energy & PV System Maintenance
Focusing on predictive maintenance, we developed intelligent computer vision systems for automatic fault detection in photovoltaic (PV) installations. These AI-based solutions identify and classify defects from electroluminescence images, enabling proactive and efficient maintenance of solar energy assets.
📖 Defect Detection in PV Electroluminescence Images Using YOLOv9 to YOLOv12 Lightweight Variants, IEEE International Conference on Digital Signal Processing (DSP 2025)
Energy Disaggregation
We designed a non-intrusive load monitoring system that decomposes total energy consumption into appliance-level profiles. This enables personalized energy insights and promotes data-driven efficiency improvements across residential and industrial environments.
Future Developments
Publications & Recognition
Our scientific work is regularly presented at international conferences, ensuring that Wattnow’s innovations are both academically validated and industry-relevant.
