Electricity Demand Forecasting using Gaussian Processes

We present an electricity demand forecasting algorithm based on Gaussian processes. By introducing a task-specific, custom covariance function k_power, which incorporates all available seasonal information as well as weather data, we are able to make accurate predictions of power consumption and renewable energy production. The hyper-parameters of the Gaussian process are optimized automatically using marginal likelihood maximization. There are no parameters to be specified by the user. We evaluate the prediction performance on simulated data and get superior results compared to a simple baseline method.