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Predicting the demand for electricity with uncertainty helps in planning andoperation of the grid to provide reliable supply of power to the consumers.Machine learning (ML)-based demand forecasting approaches can be categorizedinto (1) sample-based approaches, where each forecast is made independently,and (2) time series regression approaches, where some historical load and otherfeature information is used. When making a short-to-mid-term electricity demandforecast, some future information is available, such as the weather forecastand calendar variables. However, in existing forecasting models this futureinformation is not fully incorporated. To overcome this limitation of existingapproaches, we propose Masked Multi-Step Multivariate Probabilistic Forecasting(MMMPF), a novel and general framework to train any neural network modelcapable of generating a sequence of outputs, that combines both the temporalinformation from the past and the known information about the future to makeprobabilistic predictions. Experiments are performed on a real-world datasetfor short-to-mid-term electricity demand forecasting for multiple regions andcompared with various ML methods. They show that the proposed MMMPF frameworkoutperforms not only sample-based methods but also existing time-seriesforecasting models with the exact same base models. Models trainded with MMMPFcan also generate desired quantiles to capture uncertainty and enableprobabilistic planning for grid of the future.

Masked Multi-Step Probabilistic Forecasting for Short-to-Mid-Term Electricity Demand