arXiv:2209.01378v3 Announce Type: replace Abstract: An elementary Recurrent Neural Network that operates on p time lags, called an RNN(p), is the natural generalisation of a linear autoregressive model ARX(p). It is a powerful forecasting tool for variables displaying inherent seasonal patterns across multiple time scales, as is often observed in energy, economic, and financial time series. The architecture of RNN(p) models, characterised by structured feedbacks across time lags, enables the design of efficient training strategies. We conduct a comparative study of learning algorithms for thes
The continuous advancements in AI and the increasing demand for sustainable and efficient energy management are driving innovation in forecasting methods.
Improved power consumption forecasting is critical for optimizing energy grids, resource allocation, and carbon reduction efforts, impacting critical infrastructure and economic stability.
The development of more powerful and adaptable recurrent neural networks, like RNN(p), allows for more accurate prediction of complex seasonal energy patterns, potentially leading to more efficient energy systems.
- · Energy Grid Operators
- · Smart City Developers
- · AI/ML companies specializing in forecasting
- · Traditional forecasting methods
- · Inefficient energy suppliers
More accurate power consumption forecasts enable better load balancing and reduced waste in energy distribution.
Optimized energy grids could lead to lower energy costs for consumers and industries, fostering economic growth.
Enhanced energy efficiency globally could accelerate the transition to renewable energy sources and mitigate climate change.
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Read at arXiv cs.LG