RNN(p) for Power Consumption Forecasting
The RNN(p) model extends linear autoregressive models to better forecast power consumption by using multiple time lags (up to p). This architecture is effective for energy forecasting because it captures complex seasonal patterns. Experiments on New England and London data show RNN(p) offers superior accuracy compared to other models, providing reliable predictions that help energy companies make better operational and financial decisions.