Blogging About Electricity Grids, Julia, and Machine Learning
In software development, the term “deprecated” refers to functionality that is still usable, but is obsolete and about to be replaced or removed in a future version of the software. This blog post will explain why deprecating functionality is a good idea, and will discuss available mechanisms for deprecating functionality in the Julia language.
In a previous blog post, we reviewed how neural networks (NNs) can be used to predict solutions of optimal power flow (OPF) problems. We showed that widely used approaches fall into two major classes: end-to-end and hybrid techniques. In the case of end-to-end (or direct) methods, a NN is applied as a regressor and either the full set or a subset of the optimization variables is predicted based on…
In a previous blog post, we discussed the fundamental concepts of optimal power flow (OPF), a core problem in operating electricity grids. In their principal form, AC-OPFs are non-linear and non-convex optimization problems that are in general expensive to solve. In practice, due to the large size of electricity grids and number of constraints, solving even the linearized approximation (DC-OPF) is…
Another year has passed and another JuliaCon has happened with great success. This was the second year that the conference was fully online. While it’s a shame that we don’t get to meet all the interesting people from the Julia community in person, it also means that the conference is able to reach an even broader audience. This year, there were over 20,000 registrations and over 43,000 people…
This is the final post in our series about multi-output Gaussian process (GP) models. In the first post, we described how to generalise single-output GPs to multi-output GPs (MOGPs). We also introduced the Mixing Model Hierarchy (MMH), as a way to classify and organise a large number of MOGP models from the literature. In the second post, we discussed the Instantaneous Linear Mixing Model (ILMM),…
In an earlier blog post, we discussed the power flow problem, which serves as the key component of a much more challenging task: the optimal power flow (OPF). OPF is an umbrella term that covers a wide range of constrained optimization problems, the most important ingredients of which are: variables that optimize an objective function, some equality constraints, including the power balance and…
In our previous post, we explained that multi-output Gaussian processes (MOGPs) are not fundamentally different from their single-output counterparts. We also introduced the Mixing Model Hierarchy (MMH), which is a broad class of MOGPs that covers several popular and powerful models from the literature. In this post, we will take a closer look at the central model from the MMH, the Instantaneous…
This is the first post in a three-part series we are preparing on multi-output Gaussian Processes. Gaussian Processes (GPs) are a popular tool in machine learning, and a technique that we routinely use in our work. Essentially, GPs are a powerful Bayesian tool for regression problems (which can be extended to classification problems through some modifications). As a Bayesian approach, GPs provide…
If you are someone who feels comfortable using code to solve a problem, answer a question, or just implement something for fun, chances are you are relying on open source software. If you want to contribute to open source software, but don’t know how and where to start, this guide is for you.
During the final weeks of my internship with Invenia, while looking back on my time here, I had the idea to share some thoughts on my experience and what advice I wish I had been given leading up to my start date. There are countless recipes for a successful internship, and I hope what follows can help guide you towards making yours great.