Load Forecasting

28 papers with code • 0 benchmarks • 1 datasets

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Most implemented papers

Deep Adaptive Input Normalization for Time Series Forecasting

passalis/dain 21 Feb 2019

Deep Learning (DL) models can be used to tackle time series analysis tasks with great success.

Transfer Learning in Deep Learning Models for Building Load Forecasting: Case of Limited Data

shomerthesec/Research_timeseries_forecasting_using_ashrae_dataset 25 Jan 2023

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

hazooree/lfrt Conference 2016

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.

Fast and Accurate Time Series Classification with WEASEL

sktime/sktime 26 Jan 2017

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.

Short-term Load Forecasting with Deep Residual Networks

yalickj/load-forecasting-resnet 30 May 2018

We present in this paper a model for forecasting short-term power loads based on deep residual networks.

Deep Learning for Time Series Forecasting: The Electric Load Case

albertogaspar/dts 22 Jul 2019

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

DataLabUPO/CVOA_academic 30 Mar 2020

A novel bioinspired metaheuristic is proposed in this work, simulating how the coronavirus spreads and infects healthy people.

N-BEATS neural network for mid-term electricity load forecasting

boreshkinai/nbeats-midterm 24 Sep 2020

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

manastahir/Short-Term-Load-Forecasting 28 Nov 2020

Electricity load forecasting enables the grid operators to optimally implement the smart grid's most essential features such as demand response and energy efficiency.