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@@ -8,6 +8,18 @@ Our GitHub Repo for the [AIHack2022](https://2022.aihack.org) hackathon.
 
 To see our final model and conclusions read `submission.ipynb` or our `Hackathon22` presentation.
 
+## Project Description
+
+* We attempted to optimise the cumulative profit from a battery that can charge and discharge (selling and buying energy at a time varying price).
+* We trained the algorithm by rewarding it if the price it sold at was higher than the "expected price" for the current day, which we took to be the median price from the previous 24 hours. The sliding window median tracks the baseline price of energy over a 24 hour period while averaging over the daily fluctuations, without being biased by outliers of exceptional surge pricing.
+* The model was trained on the first month of 2019 and then tested on the remaining dataset.
+* We found that for the default hyper-parameters, that our trained model significantly outperformed a random walk in charge and discharge, obtaining a net profit of roughly £15000.
+* The strategy adopted by the model was to buy low and sell high, which was especially profitable during the exceptional surge pricing.
+* Further improvements to the project would have been:
+    * To tune the model hyper-parameters by using a Bayesian optimisation algorithm. This is an appropriate tool as Bayesian optimisation can efficiently minimise a costly to evaluate function in many dimensions.
+    * To improve the reward assignment so that the agent was more inclined to sell at peak prices, perhaps by including a gradient estimation of the current price.
+    * To improve upon the forecasting method to estimate the price baseline for the next 24 hour period, rather than using the median of the previous 24hr period.
+
 ## Installation
 
 Install the conda environment by running: