Our project contributes to renewable energy management and decentralised energy transition by implementing an Artificial Intelligence (AI) powered solution to forecast and optimise Renewable Energy Communities (RECs).
Can you describe your project in a few words?
Our project contributes to renewable energy management and decentralised energy transition by implementing an Artificial Intelligence (AI) powered solution to forecast and optimise Renewable Energy Communities (RECs). It predicts renewable energy production and consumption patterns, focusing on high-frequency, low-latency micro-site predictions for multiple energy sources such as solar and wind power. These predictions can facilitate effective energy management, enabling communities to optimally balance internal and external energy flows, share energy efficiently, and contribute to a greener and more sustainable energy landscape.
Who will help implement the AI solution?
The AI solution is implemented by a highly specialised and dedicated team, which comprises PhDs, MBAs, talented data scientists, machine learning experts, software developers, and energy domain specialists. Furthermore, we engage with various stakeholders, including, but not limited to, local Renewable Energy Communities, technology providers, energy management system vendors, and regulatory bodies. Their insights, coupled with our team's expertise, ensure our AI solution's successful implementation, integration, and widespread adoption.
What is the AI solution the project plans to implement?
The FlexyGrid AI solution comprises state-of-the-art deep learning, conventional statistical modelling, and advanced reinforcement learning methodologies. We harness the power of these approaches to create a hybrid forecasting model capable of capturing the complex and variable nature of renewable energy patterns.
Our system ingests data from diverse sources like weather stations, IoT devices, smart meters, and user-generated inputs. Following data cleaning and pre-processing, the engine performs feature engineering to extract meaningful insights and normalise the data, as well as learning techniques to combine individual forecasting models, generating more accurate and robust predictions. Our solution offers a RESTful API and an intuitive user interface for end-users to ensure smooth integration with existing energy management systems. By leveraging cutting-edge containerisation technologies such as Docker and Kubernetes, we provide our solution can be deployed at scale, efficiently and reliably.