This challenge is provided by Schneider Electric
Flexibility in energy management is essential to avoid costly reinforcements of the power system and to maintain secure
supply while increasing the penetration of renewable sources.
This is a delicate balance, where algorithms can help battery charging systems to be as efficient as possible
For instance, buy more energy when its price is lowest, and buy less or sell energy when its price is highest.
Challenge: Develop an energy demand forecasting system to help automated energy management systems and building managers reduce costs.
Our Online Classes
What you will learn:
- End-to-end AI development process
- Hands-on experience developing a forecasting system
- Neural Networks techniques for time-series forecasting
- Feature engineering techniques for machine learning
Join an online class. Seats are limited.
According to the World Health Organization, 390 million people are infected by dengue every year, with 2 out of 5 children at risk of being infected.
The Americas, South-East Asia and Western Pacific regions are the most seriously affected, with Asia representing ~70% of the global burden of disease.
Challenge: develop an accurate forecasting system that can predict dengue outbreak 8 to 16 weeks in advance for effective preventive measures.
Diminishing the dengue danger: predicting future dengue outbreaks using ML.
Forecasting dengue cases in Singapore 8 weeks in advance.