This is a Starter notebook for the SPEOAU data hackathon open to SPEOAU community.
hack link: https://zindi.africa/competitions/speoau-data-hackathon
With recent advances in the Energy sector in Nigeria, forecasting in energy markets is identified as one of the highest leverage contribution areas of Machine/Deep Learning toward transitioning to a renewable-based electrical infrastructure.
The objective is to build a machine-learning model capable of predicting the Actual Price of Electricity at a given time.
Efficient Resource Planning: Accurately forecasting electricity demand and price can help utilities and energy companies plan and manage their resources more efficiently. By predicting demand and price in advance, energy companies can optimize their power generation and distribution strategies to minimize wastage and reduce costs.
Improved Grid Stability: Accurate demand and price forecasting can also help improve the stability of the electrical grid. By predicting demand and price fluctuations, energy companies can proactively adjust their generation and distribution networks to prevent outages and ensure that the grid operates smoothly.
SPE- OAU is a student chapter member of Society of Petroleum engineers, the world's largest professional network of Oil & Gas/ Energy professionals serving 160,000 plus engineers, scientists, academia, etc.
Chemotronix is an early-stage climate tech startup passionate about developing clean energy technologies and digital solutions to ensure net zero emissions.