The art of accurate load forecasting for optimal grid management
Grid operators need to be able to predict with extreme accuracy where grid hotspots will occur, in order to identify infrastructure reinforcement needs, plan distribution, manage assets and ameliorate day-ahead or intraday planning.
A particularity that makes load forecasting extremely challenging is that energy consumption is extremely volatile, far more volatile than any other commodity. Another feature of electricity is that the spikes’ intensity is non-homogenous in time. Load spikes are mostly observed during specific time windows. Although these spikes usually have a very short duration, the impact on the system is extreme if no measures are taken. Load forecasting on an aggregated basis can help limit and manage these abnormalities. For the grid operator to deal with such spikes usually cost-inefficient actions are required, so prior knowledge will help for more effective asset management from his side.
Traditional load forecasting models take into account historical and weather data to predict the electricity demand. With the extensive renewable sources’ injection into the modern grid though, it’s much harder to make accurate load predictions.
New technologies can deliver more accurate load forecasting by taking into account not only historical and local weather data but also smart meter data, dates, zip codes and other inputs which can result in more than 98% accuracy. The difference between a 95% accurate load forecasting and a 98% one translates into millions of revenue losses for utilities and grid operators.
Distribution and transmission planning need a short-term forecasting intraday or day ahead. Intraday forecasting is usually achieved for the next 60 minutes based on 1-minute data samples from real-time consumption data. And it’s also possible to detect solar energy production and EV electricity demand. Whereas a day-ahead forecaster can be achieved based on 15–60-minute data samples.
Accurate load forecasting is a real art that can have a huge impact on the planning and finances of grid operators.