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- W4321012238 abstract "Predicting the demand for electricity with uncertainty helps in planning and operation of the grid to provide reliable supply of power to the consumers. Machine learning (ML)-based demand forecasting approaches can be categorized into (1) sample-based approaches, where each forecast is made independently, and (2) time series regression approaches, where some historical load and other feature information is used. When making a short-to-mid-term electricity demand forecast, some future information is available, such as the weather forecast and calendar variables. However, in existing forecasting models this future information is not fully incorporated. To overcome this limitation of existing approaches, we propose Masked Multi-Step Multivariate Probabilistic Forecasting (MMMPF), a novel and general framework to train any neural network model capable of generating a sequence of outputs, that combines both the temporal information from the past and the known information about the future to make probabilistic predictions. Experiments are performed on a real-world dataset for short-to-mid-term electricity demand forecasting for multiple regions and compared with various ML methods. They show that the proposed MMMPF framework outperforms not only sample-based methods but also existing time-series forecasting models with the exact same base models. Models trainded with MMMPF can also generate desired quantiles to capture uncertainty and enable probabilistic planning for grid of the future." @default.
- W4321012238 created "2023-02-17" @default.
- W4321012238 creator A5047100090 @default.
- W4321012238 creator A5051396557 @default.
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- W4321012238 date "2023-02-13" @default.
- W4321012238 modified "2023-09-29" @default.
- W4321012238 title "Masked Multi-Step Probabilistic Forecasting for Short-to-Mid-Term Electricity Demand" @default.
- W4321012238 doi "https://doi.org/10.48550/arxiv.2302.06818" @default.
- W4321012238 hasPublicationYear "2023" @default.
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