Research on Machine Learning applications in Smart Grid & Microgrids

Smart grids and microgrids are the future of the power system we use today. Integrating the power systems with communication devices and adding a data layer has its perks for power system operators as well as the consumers.

This novel power system will generate enormous amounts of data with its maturity. As researchers, it is quite fascinating for us since this means that we get to combine two disciplines, Machine Learning & Power Systems.

In this blog post, I will list all the possible applications that I come across of machine learning in power systems.

Update: I will start to categorize them into,

Microgrid research
Energy forecasting
Smart grid research

Microgrid research

Multi-agent systems for microgrid control [link]
Reinforcement learning for microgrid energy management
Detect security breaches
Fault detection and classification
Microgrid islanding detection
Multi-agent reinforcement learning for microgrid control

Energy forecasting research

Solar energy forecasting / Solar irradiation forecasting
Power system demand forecasting

Smart grid research

Detect security breaches
Fault detection and classification
Data compression in smart grids [link]

Comments

How to program in R language in Jupyter Notebooks using Conda environments

In this blog post, we will discuss how to use conda environments to install the R kernel and program in R in Jupyter notebooks. 1. Install conda The first step in using conda environments is to install conda itself. You can download the conda installer from the Anaconda website and follow the instructions to install it on your system. Once you have conda installed, you can create and manage virtual environments. 2. Create a new conda environment To create a new conda environment, you can use the following command in your terminal: conda create -n myrenv This will create a new environment called "myenv" that you can use to manage your packages and dependencies. You can activate this environment by using the following command: conda activate myrenv 3. Install the R kernel Now that you have an active conda environment, you can use it to install the R kernel. You can do this by using the following command (you may have to press Enter key several times to finish the process): c...

Cracking the Black Box: Six Lenses for Understanding Deep Learning

Deep learning has revolutionised technology, giving us everything from uncannily smart chatbots to medical imaging that can spot diseases better than the human eye. Yet, for a long time, a central mystery has haunted the field: why do these enormous models work so well? According to classical statistics, a model with billions of parameters—far more than its training data—should fail spectacularly. It ought to memorise the data, noise and all, and be unable to generalise to new, unseen examples. But deep neural networks defy this wisdom. They generalise brilliantly. How do we explain this apparent magic? There isn't one single answer. Instead, researchers view the problem through several different theoretical "lenses." Here are six of the most important ones. 1. The Linearisation Lens: The Neural Tangent Kernel (NTK) ⚙️ The NTK offers a startling insight: what if, under the right conditions, a massively complex neural network is just a simple, linear model in disguise? The...

Webinar Series on Smart Grid and Microgrids by Smart Grid Research Lab, University of Moratuwa

This webinar series is created by the Smart Grid Research Lab of the University of Moratuwa , Sri Lanka. As a research assistant and a Ph.D. student, I had the honor of organizing the series. The webinar series is titled "Discussion forum on Smart Grid and Microgrids". It will cover the fundamentals of Smart Grid and Microgrids and the research areas of Smart Grid Research Lab. The webinars will be uploaded to YouTube and created as a playlist . Check out the introduction to the webinar series and the lab by Prof. K. T. M. U. Hemapala.

Kalhan's Blog

Search This Blog