Conventional load forecasting techniques obtain single-value load forecasts by exploiting consumption patterns of past load demand.
Electricity load forecasting enables the grid operators to optimally implement the smart grid's most essential features such as demand response and energy efficiency.
The COVID-19 pandemic has had a significant impact on society, both because of the serious health effects of COVID-19 and because of public health measures implemented to slow its spread.
Accurate forecasting is important for decision-makers.
A novel bioinspired metaheuristic is proposed in this work, simulating how the coronavirus spreads and infects healthy people.
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.
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.