The prices of electricity differ significantly between peak hours (peaks) and down times (valleys). The electricity available within a power grid will be wasted if not used during valleys, and there may not be sufficient power to meet the demand the during peaks. What can we do to balance the supply and demand of electricity?

1. Use historical data to predict future electricity prices and evaluate the accuracy of the prediction
2. Determine the exact timing of buy and sell decisions using the predictions
3. Simulate electricity charging and discharging using a Dyson Vacuum Cleaner
4. Calculate average costs and profit from the buy and sell decision

1. Analyzing historical electricity price data to develop a model that can predict future electricity prices.

2. Conducting research to design a model for buying and storing electricity during surplus periods and selling during electricity shortages, aiming for cost-efficiency.

3. Identifying precise timing for buy and sell decisions based on real-time data to maximize effectiveness.

In other words, I researched historical electricity price data as input for my code to generate new electricity price predictions, and then created graphs and a conclusion to easily demonstrate when to buy and sell electricity.

https://docs.google.com/spreadsheets/d/1mF-ASOCsdFgmDng7y3MRrhfJf2QaPS1v/edit?usp=sharing&ouid=101704921617860491293&rtpof=true&sd=true

https://docs.google.com/presentation/d/1KfdkSFC_jRTezGJlo5SFhx7MnEiNlF2q/edit?usp=sharing&ouid=101704921617860491293&rtpof=true&sd=true

Theoretically, we can balance electricity supply and demand to certain extent, in this case, using Tesla Batteries and my prediction model in certain parts of the world and make profits from it, even when we're sleeping! We can also balance the supply and demand by charging during off-peak hours and release the energy during peak hours. 

https://www.ceicdata.com/zh-hans/turkey/environmental-environmental-policy-taxes-and-transfers-oecd-member-annual/tr-industry-electricity-price-usd-per-kwh

Vasudharini Sridharan, Mingjian Tuo and Xingpeng Li, "Wholesale Electricity Price Forecasting using Integrated Long-term Recurrent Convolutional Network Model",https://arxiv.org/abs/2112.13681.

https://www.youtube.com/watch?v=AsNTP8Kwu80&ab_channel=StatQuestwithJoshStarmer

https://www.youtube.com/watch?v=YCzL96nL7j0&ab_channel=StatQuestwithJoshStarmer

https://www.youtube.com/watch?v=HZGCoVF3YvM&ab_channel=3Blue1Brown

https://www.youtube.com/watch?v=lG4VkPoG3ko&ab_channel=3Blue1Brown

https://www.youtube.com/watch?v=5NMxiOGL39M&ab_channel=BrandonRohrer

https://www.youtube.com/watch?v=NIqeFYUhSzU&ab_channel=WoodyLewenstein

https://www.youtube.com/watch?v=UoH2-TlcDrU&ab_channel=TED-Ed

I would like to thank my teacher, Ms. Cole, for telling me about this Science Fair and giving me permission to join. It has been so much fun doing experiments and learning new things along the way!

I am also grateful to my parents, who have patiently supported me, even if it meant allowing me to stay up to work on experimenting.

Special thanks to CYSF, this platform! I got a lot of helpful tips about presentation day, judging, etc.

Thank you all for your support, encouragement, and belief in my work. This project would not have been possible without you.