Machine Learning Group (MLG)

Welcome to the Machine Learning Group (MLG) at the Calculus Energy. We are a highly versatile and a proactive group of researchers working on supervised and unsupervised learning, with a focus on energy applications (building energy consumption forecasting, solar PV estimation). Our interests within machine learning spans theoretical foundations, automatic differentiation, derivative and black box optimization algorithms - applied to a variety of industrial applications.

The nature of our industrial projects range from studying fundamental concepts in applied Bayesian statistics, all the way to getting our algorithms to perform competitively against the state-of-the-art in big-data applications. We also work on a broad range of application domains, including but not limited to: energy modelling, material science, finance, social networks, and chemical simulations, just to name a few. If you are interested in finding out more about our R&D activities and our bespoke AI products, please get in touch with your profile and the application domain (i.e the problem definition) you are interested in.

Control Engineering and Optimization

Dynamics, feedback, stability, optimisation — these and other related concepts are core to control science and engineering and are at the heart of the smart energy grid. Indeed, one could say that it is only a partial exaggeration to conclude that the smart grid is, at its core, is a control and optimisation problem.

At Calculus Energy, we strongly believe that control engineering is a key enabling technology for the deployment and higher penetration of renewable energy systems. Solar and wind power require advanced control techniques for high-performance and reliable operation. On the other hand, better predictive models of renewable generation are needed at all levels (which our Numerical Analysis group works on), right from component to systems. Therefore, the management of generation and load is now a multi-variable, non-convex optimisation problem with numerous constraints, and decision and control schemes are needed that factor in this complexity. We apply optimal design and engineering systems operation methodology to energy systems (storage, generation, distribution, and smart devices), and risk analysis. As a natural extension to this, our control and optimisation group also work within the multidisciplinary fields of signal processing, statistics, and machine learning as a method for fitting parametric models to observed data.

Application example: HVAC and setpoint optimization in buildings

Commercial buildings are often required to save energy and will do so in part by operating and maintaining heating and cooling systems in the most overall energy efficient and economical manner possible. To efficiently achieve energy savings, Heating, Ventilation, and Air Conditioning (HVAC) systems controlled by an Energy Management Control System (EMCS) should operate to maintain appropriate temperatures and humidity levels according to facility usage type and schedules. Typically, these are called setpoints and require to the appropriately chosen either dynamically by the control system or statically set at pre determined levels to save energy.

We model the building energy dynamics using neural networks (our so called traditional model uses EnergyPlus), which leverages large volumes of data to represent the complex dynamics of buildings - which we model without any underlying assumptions. Further, we deploy an input/output optimization algorithm which efficiently find the optimal control inputs for the model represented by an artificial intellegence model. This allows for absolute real time control of the HVAC system. Typically, we have seen that this approach reduces energy consumption by atleast 10% in commercial buildings.

Get in touch with us to see how much you can save on your energy bill, by simply tuning your setpoints.

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