In the research projects we developed the following apps to support energy communities, prosumers, and market participants in making data-driven and sustainable energy decisions.
From forecasting electricity prices to optimizing solar generation and community planning, each tool leverages advanced analytics, machine learning, and real-world data to enhance flexibility, efficiency, and profitability in modern energy systems.
| Application | Description | Access Link |
|---|---|---|
| Electricity Price Prediction (DAM & IDC) | A forecasting platform for day-ahead and intra-day electricity market prices. It uses advanced machine learning and transformer-based models to provide accurate short-term and ultra-short-term price predictions, supporting trading and scheduling decisions. | Access App |
| Energy Community Simulator – ECOSIM | A planning and simulation tool for designing and optimizing energy communities. It evaluates technical, economic, and environmental performance, helping users compare tariff schemes, PV sizing, and battery strategies. | Access App |
| Smart iPV Dashboard (PV Forecast) | A forecasting dashboard providing short-term solar power generation predictions based on weather and irradiance data. It supports prosumers and aggregators in maximizing PV self-consumption and grid interaction efficiency. | Access App |
| Prosumer’s Assistant (Alexa Energy Assistant) | A voice-enabled energy management assistant integrated with Amazon Alexa. It assists prosumers in monitoring consumption, optimizing scheduling, and participating in local electricity markets using reinforcement learning strategies. | Access App |