13d (edited) • 💻 Technical Skills
New Online Course: Reinforcement Learning for Energy
In this course, we are developing a model in Python that uses Reinforcement Learning to minimize the operating costs of a Smart Building (also known as an 'energy hub'). The course is In the 'Classroom' at the very end (numbered 117). The full Python code is available for download. Definitely watch the videos as they explain all details necessary.
Below is an explanation (using simple language with all jargon explained). Also you can download a slide in PDF (the slides in PowerPoint format are inside the course).
I believe the best way to truly understand Machine Learning and Reinforcement Learning is by diving straight into the code. The code in these videos is based on a real-world application where I tested various methodologies to determine the most cost-effective way to operate a smart building.
This building is 'smart' because of how it manages energy. It is equipped with its own energy storage unit (a battery), a solar photovoltaic (PV) unit, and a central computer system. Using advanced algorithms, this system autonomously decides:
- When and how much to charge or discharge the battery.
- Whether to curtail (reject) excess energy output from the solar PV unit.
- When and how much electricity to purchase from the main grid.
Over time, we have explored different algorithms for this central computer system. We covered Monte Carlo methods in Course 115 and Stochastic Optimization in Course 116. This new course (Course 117) focuses specifically on Reinforcement Learning.
Reinforcement Learning is a subfield of Machine Learning. While traditional Machine Learning models learn by studying historical datasets, Reinforcement Learning trains an algorithm to learn through trial and error—interacting with an environment to figure out the best sequence of actions to achieve a goal.
Machine Learning is a subfield of AI, and Reinforcement Learning is a specific branch of Machine Learning. Machine Learning models learn by studying datasets.
It's called Reinforcement Learning similar to behavioral psycholog which says that we are learning through rewards and penalties. When the AI (the "agent") takes an action that gets it closer to its goal (in our case: saving money in the smart building) it receives a mathematical "reward." This positive feedback reinforces that specific behavior of the algorithm. Reinforcement Learning trains an algorithm (which is known as Agent) to learn through trial and error.
Specifically, we are building a single-agent Reinforcement Learning model. The 'agent' (our RL algorithm) acts as the brain of the building. It is continually observing the environment (like solar output and battery levels) and sees the financial consequences of its choices. This way the agent learns over time how to make the best possible independent decisions.
33
5 comments
New Online Course: Reinforcement Learning for Energy
powered by
skool.com/software-school-for-energy-7177
Launch your energy sector career: energy economics with data science & ML, real interview prep, job ads, industry reports, 1-on-1 mentorship.
Suggested communities
Powered by