When thousands of devices power the world’s largest experiments, even a single failure can ripple across the system. At CERN, the smooth operation of accelerators and experiments relies on the control infrastructure, which comprises thousands of technical devices working in unison. These include programmable logic controllers (PLCs), which run industrial control logic, power supplies that feed magnets and detectors, and front-end modules that manage communication between equipment and higher-level systems.

Imagine a factory or research laboratory filled with machines, each equipped with its own sensors and controllers. Traditionally, data from these devices is sent to central servers for processing. While effective, this approach often introduces delays and network bottlenecks. Industrial Edge Computing addresses the problem by bringing computation closer to where data is generated. Instead of relying on distant servers, applications run directly on edge devices installed near the equipment, allowing for real-time decision-making. These industrial PCs can host applications that collect diagnostics, perform analytics, or even take direct control of equipment. For example, a ventilation system can adjust airflow more efficiently by running advanced control algorithms locally on an edge device using nearby sensor readings.

Large technical systems such as cooling plants, ventilation systems, and safety installations must operate continuously around the clock. These systems rely on programmable logic controllers (PLCs), specialized industrial computers built to execute precise instructions reliably for years without interruption. Designed with safety and stability in mind, PLCs form the backbone of industrial automation. They manage essential functions, such as circulating cooling water for accelerators, adjusting ventilation fans to maintain safe conditions in underground tunnels, and activating interlocks to shut down equipment immediately if unsafe conditions are detected.

At CERN’s CMS experiment, data quality has traditionally been certified by human operators reviewing run after run. However, with the increasing volume of data, this manual process is becoming a bottleneck, paving the way for machine learning to take a more prominent role.

To ensure datasets are reliable for physics analyses, the CMS collaboration uses Data Quality Monitoring (DQM) software. This software analyzes raw detector output and generates concise summaries, known as monitor elements. These include histograms of sensor signals, basic statistics about detector performance, and plots that highlight unusual or unexpected behavior.

Every second, the CMS tracker detector records millions of particle trajectories. But without careful quality checks, this flood of data risks being unusable for physics discoveries. To guard against this, CMS relies on a dedicated Data Quality Monitoring (DQM) system. The tracker, functioning like a giant digital camera, captures the paths of charged particles produced during collisions. Because it generates such an enormous volume of data, ensuring quality is essential for reliable physics analyses.

The transition to renewable energy is not only about adding more wind turbines or solar farms. It also requires making demand more flexible so that consumption can adjust to the availability of renewable power. At Shared Electric GmbH, I worked on three interconnected projects that demonstrated demand flexibility in action:

1. Building a low-cost IoT device to measure household consumption.
2. Developing an interactive tool to show the value of load flexibility.
3. Designing a demand-side management platform to connect utilities and consumers. Together, these projects demonstrated how technology can link consumers, utilities, and the grid to create smarter, cleaner energy systems.

Electricity grids face a growing challenge: demand rarely matches supply. Evening peaks arrive just as solar output drops, while midday often brings excess solar generation when demand is modest. Traditionally, utilities have bridged this gap with fossil fuel plants, an approach that is both costly and carbon-intensive. As renewable energy continues to scale, a smarter path is emerging in the form of demand-side management (DSM). By shifting when and how people use energy, DSM helps bring demand closer to supply. Instead of treating consumers as passive users, it engages them directly in balancing the grid.

While DSM is effective in theory, people need to be actively involved to make it work at scale. At Shared Electric, that became our starting point for building a pilot system called Engaze. It enabled utilities to request small shifts in energy usage while engaging consumers through timely nudges via app notifications. The system served as a testbed to demonstrate how behavioral demand response could work in practice.

Electricity powers nearly everything we do, yet most of us rarely think about when we use it. Behind the scenes, demand rises and falls every hour, and the grid works constantly to keep supply in balance. What remains invisible to households are the timing, costs, and emissions, the factors that hold the key to making energy both cheaper and cleaner.

Imagine reducing your electricity bill and cutting CO₂ emissions simply by running your washing machine a few hours later. Take laundry as an example: shifting a wash cycle from the busy 6 pm slot to 3 pm not only leverages solar generation and lower costs but also lightens the evening demand on the grid. The result is real savings for households and a lighter footprint on the environment. When scaled across millions of homes, the collective impact becomes significant. The real challenge, however, lies in making these benefits visible, as most utilities and consumers rarely recognize just how much money and carbon can be saved by shifting demand.

Modern energy grids are getting smarter, yet they still lack a key aspect: real-time data on household electricity use. Without it, much of the renewable energy ends up wasted, and the grid has to rely on fossil fuels during peak hours. While working at Shared Electric GmbH, a renewable energy startup, Shaaz and I built a lightweight IoT device to make electricity monitoring accessible and affordable for households and utilities.

Using Nutshell isn’t just about adding scripts.
It’s about making explanations expandable so readers can dive deeper when they want.
:What is Nutshell?