On the popular UCR benchmark of 85 TS datasets, WEASEL is more accurate than the best current non-ensemble algorithms at orders-of-magnitude lower classification and training times, and it is almost as accurate as ensemble classifiers, whose computational complexity makes them inapplicable even for mid-size datasets.
Management and efficient operations in critical infrastructure such as Smart Grids take huge advantage of accurate power load forecasting which, due to its nonlinear nature, remains a challenging task.
We present in this paper a model for forecasting short-term power loads based on deep residual networks.
Conventional load forecasting techniques obtain single-value load forecasts by exploiting consumption patterns of past load demand.
A developing country like Pakistan with sizable pressure on their limited financial resources can ill afford either of these two situations about energy forecast: 1) Too optimistic 2) Too conservative.