Peak shaving optimisation in school kitchens : A machine learning approach

With the increasing electrification of todays society the electrical grid is experiencing increasing pressure from demand. One factor that affects the stability of the grid are the time intervals at which power demand is at its highest which is referred to as peak demand. This project was conducted in order to reduce the peak demand through a process called peak shaving in order to relieve some of this pressure through the use of batteries and renewable energy. By doing so, the user of such systems could reduce the installation cost of their electrical infrastructure as well as the electrical billing. Peak shaving in this project was implemented using machine learning algorithms that predicted the daily power consumption in school kitchens with help of their food menus, which were then fed to an algorithm to steer a battery according to the results. All of these project findings are compared to another system installed by a company to decide whether the algorithm has the right accuracy and performance. The results of the simulations were promising as the algorithm was able to detect the vast majority of the peaks and perform peak shaving intelligently. Based on the graphs and values presented in this report, it can be concluded that the algorithm is ready to be implemented in the real world with the potential to contribute to a long-term sustainable electrical grid while saving money for the user.