This code is uses machine learning in Python to optimize the design of an energy system. The energy system is defined by two variables: the energy input (in MW) and the efficiency of the system (dimensionless). The goal is to predict the cost of the energy system as a function of these variables.

To do this, the code first defines a function called `cost`

that calculates the cost of the energy system as a function of the energy input and the efficiency. This function takes into account the cost of the energy input itself, as well as the efficiency of the system (since a more efficient system will have a lower cost).

Next, the code generates a training dataset by randomly sampling a large number of values for the energy input and efficiency. The machine learning model (a random forest regressor) is then trained on this dataset using the `fit`

function.

The code then generates a prediction dataset and uses the machine learning model to predict the cost for this dataset using the `predict`

function. The resulting predictions are plotted as a function of the energy input, with the starting energy input indicated by a red dot. The resulting plot can be used to visualize how the predicted cost of the energy system changes as a function of the energy input and efficiency.

```
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Dec 21 23:01:06 2022
@author: ramnot
"""
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor
# Define variables for the energy system
energy_input = 10 # MW
energy_output = 8 # MW
efficiency = energy_output / energy_input # dimensionless
# Define the cost function
def cost(energy_input, efficiency):
# Assume a cost of $1000/MW for energy input
return energy_input * 1000 + efficiency**2
# Generate a training dataset
num_samples = 1000
X_train = np.random.uniform(0, 20, (num_samples, 2))
y_train = cost(X_train[:,0], X_train[:,1])
# Use machine learning to predict the cost as a function of the energy input and efficiency
model = RandomForestRegressor()
model.fit(X_train, y_train)
# Generate a prediction dataset
energy_input_range = np.linspace(0, 20, 100)
X_pred = np.column_stack((energy_input_range, np.ones_like(energy_input_range)*efficiency))
# Use the machine learning model to predict the cost for the prediction dataset
y_pred = model.predict(X_pred)
# Plot the predicted cost as a function of the energy input
fig, ax = plt.subplots(figsize=(8,6))
ax.plot(energy_input_range, y_pred)
ax.plot(energy_input, cost(energy_input, efficiency), 'o', color='red', label='Starting energy input')
ax.set_xlabel('Energy input (MW)', fontsize=12)
ax.set_ylabel('Cost (USD)', fontsize=12)
ax.legend(fontsize=12)
plt.show()
```