Our technology ecosystem allows us to cover the key components in the evolution of energy grids and provides solutions fit for any purpose within this continuum. With advanced hardware abstraction, and supported by the latest AI and Machine Learning technology tools, our Asset Management Platform provides a diverse set of subsystems that support all the Amp X solutions.
Our platform manages and allows for coordination between the various functions provided by our Smart Tx and DLS solutions combining their data and learning with those from front of meter assets. This enables the interoperability of the assets from front of meter to behind- collecting and presenting all the relevant energy data, monitoring performance or maintenance alerts, managing flexibility provision, market bidding and energy dispatch.
At the core of our platform is a cloud-based aggregation and analytics capability for monitoring, control and optimisation of a large and diverse set of generators and loads.
The platform is aware of the grid topology and market, therefore it can leverage the specific capabilities of the distribution network, through cutting-edge AI and Machine Learning technologies. This way, we are able to optimise and balance the grid, as well as utilise network effects to greatly enhance controllability and minimise the effects of grid congestion.
From the Amp X Smart Tx, to community batteries, EVs and smart homes, our platform abstracts away the complexities of each individual piece of hardware, providing a powerful means of monitoring and dynamically managing them - individually and collectively.
ALICE
Alice employs ‘Internet of Things’ (IoT) technology for dynamic load shaping, primarily for the provision of flexibility, but also for energy, cost and CO2 optimisation.
The way Alice works differs from current ‘smart home’ approaches, where the user defines a schedule that the system has to follow. Our autonomous system determines the schedule based on user preferences and behaviours observed over time. The user need not actively engage with the system after an initial setup - the system works to save and even earn the user money behind-the-scenes, and only requests the user’s attention when new opportunities might require changes in user behaviour or expectations.
Whether the end-user wants to control the devices in their factory, office or home, Alice is easy to set up and needs little input to operate. Devices are categorised and grouped based on their capacity to provide flexibility rather than by hardware type, and our innovative ‘edge autonomy’ approach to control ensures that Alice always takes care of the user and the devices under its control first.
Powered by our Aggregation Management Platform ecosystem, Alice brings users further avenues for energy cost reduction, including the option to participate in demand response programs. Alice identifies and forecasts available flexibility in an autonomous way, and our Asset Management Platform aggregates this flexibility with that of other users to provide services into a number of markets. By participating in this way, Alice can further optimise an end-user's cost with no noticeable impact to the user’s experience.
Alice's robust analytics engine continuously analyses user behaviour, device performance, and market conditions. This data-driven approach not only enhances user experience but also enables more precise energy management, contributing to cost savings and environmental sustainability.
ARTIFICIAL INTELLIGENCE & MACHINE LEARNING
Foresight built into our ecosystem
From weather, price and load forecasting to predictive maintenance, our state-of-the-art AI and machine learning engines underpin most of what our Amp X ecosystem does. The heterogeneous forecasting needs of our Aggregation Management Platform are met by a variety of machine learning approaches, including uni/multivariate and exogenous variable time series models, random forest and boosting tree ensemble models, and a range of deep neural network architectures.
Our data-rich technologies provide us with high-quality data on which to train our predictive models. These models range from market-specific price forecasting to predictive maintenance schedules and user behaviour modelling. The range of forecasts and models underpin our ecosystem's functionality, from market participation and bidding to on-device control and automation.
The autonomy approach to our technology relies on well-defined adaptive control which requires edge analytics. This approach ensures that our devices can make the best decisions with the highest possible certainty while still maintaining the privacy and security of our users by not sending information through the network. Our Smart Tx performs analytics on the edge and its models can be updated and improved as more opportunities become available to our users in the rapidly changing energy market.
At a higher level, network modelling driven by load and weather forecasting in the asset management platform allows us to take advantage of a holistic view of the low voltage distribution network, whilst utilising network effects to our advantage. Crucially, the Amp X Asset Management Platform can be configured with location and/or network information to enable grid effects to be estimated and accounted for. This, in turn, allows us to minimise any grid congestion effects and establish hyperdynamic local pricing.
Our platform provides a central system on which data can be gathered and presented for training and analytics. This allows us to build an understanding of any specific grid topology or market by gathering disparate data sources and building predictive models on them. Our forecasting engines are tightly integrated with our control strategies but can also be used as inputs to third-party systems constituting a product within themselves.
