Energy

Areas of interest:

• Smart Grids
• Energy Storage
• Demand forecasting
• Network Design
• Energy ABM

Questions:

• How to model to improve demand forecasting
• What patterns can be identified in consumer behavior data to inform energy pricing strategies?
• How can predictive maintenance be implemented using data from smart sensors in energy infrastructure?

Techniques:

• Differential Equations: Used to model dynamic systems in energy generation and consumption. For example, they can describe the behavior of power systems over time or the thermal dynamics of energy storage systems.
• Stochastic Modeling: Involves random variables to model uncertainties in energy production (e.g., variability in solar or wind energy) and consumption.
• Agent-based modellingSimulates interactions of agents (consumers, producers, regulators) to understand complex systems and emergent phenomena in energy markets.
• Time Series Analysis: Analyzing historical data to forecast future energy demand or production trends.
• Regression Analysis: Used to model relationships between different variables, such as energy prices and consumption patterns.
• Neural Network Particularly deep learning, is applied for complex pattern recognition in large datasets, such as detecting anomalies in energy consumption or predicting equipment failures.

Dymanic pricing, incentised load management, local generation

Use green energy if on grid