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We use Generative Hamiltonian Neural Networks to model Micro Droplet Dynamics
Optimising battery charging for the electricity project. We trained a reinforcement learning algorithm to maximise cumulative profit. Please see submission.ipynb on github.
LSTM for electricity
Predictor model for whether particles will coalesce or not
We use implement models, namely Piecewise Linear Regression Model, Neural Network Model and Reinforcement Learning model, to predict the energy price based on the price in the past few days.
Buy at 11pm and Sell at 10pm.
Coalescence or not, that is a question.
Utilising machine learning and an additional dataset to optimise returns on trading generated energy with a battery.
This is a model to predict the electricity price based on spot intraday price and system price
Prepared dataset to fit into an ML method for forecasting future price
Using linear regression to predict the electricity prices. Team members: Zhuoyue Huang, Fengzhe Zhang, Yingcai Hu, Sayuan Wang
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