28 papers with code • 0 benchmarks • 1 datasets
These leaderboards are used to track progress in Load Forecasting
In order to adapt Deep Learning models for buildings with limited and scarce data, this paper proposes a Building-to-Building Transfer Learning framework to overcome the problem and enhance the performance of Deep Learning models.
LFRT, A MATLAB Toolbox for Load Forecast & Assesment of Additional Capacity for an Electrical Power System
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.
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.
Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation model
A novel bioinspired metaheuristic is proposed in this work, simulating how the coronavirus spreads and infects healthy people.
We show that our proposed deep neural network modeling approach based on the deep neural architecture is effective at solving the mid-term electricity load forecasting problem.
Short-Term Load Forecasting using Bi-directional Sequential Models and Feature Engineering for Small Datasets
Electricity load forecasting enables the grid operators to optimally implement the smart grid's most essential features such as demand response and energy efficiency.