Load Forecasting
38 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Load Forecasting
ASHRAE energy prediction III
PJM(AEP)
Most implemented papers
Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting
slaweks17/es-adrnn-with-context
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These cells enable the model to capture short-term, long-term and seasonal dependencies across time series as well as to weight dynamically the input information.
Availability Adversarial Attack and Countermeasures for Deep Learning-based Load Forecasting
To tackle this attack, an adversarial training algorithm is proposed.
Short-Term Aggregated Residential Load Forecasting using BiLSTM and CNN-BiLSTM
Using a publicly available dataset consisting of 38 homes, the BiLSTM and CNN-BiLSTM models are trained to forecast the aggregated active power demand for each hour within a 24 hr.
A Unifying Framework of Attention-based Neural Load Forecasting
In this paper, we propose a unifying deep learning framework for load forecasting, which includes time-varying feature weighting, hierarchical temporal attention, and feature-reinforced error correction.
Transformer Training Strategies for Forecasting Multiple Load Time Series
kit-iai/transformer-training-strategies
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We evaluate whether a Transformer load forecasting model benefits from a transfer learning strategy, where a global univariate model is trained on the load time series from multiple clients.
BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load Forecasting
nrel/buildingsbench
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NeurIPS 2023
We also show that fine-tuning pretrained models on real commercial and residential buildings improves performance for a majority of target buildings.
Benchmarks and Custom Package for Electrical Load Forecasting
leo-vk/proenfo
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Based on this, we conducted extensive experiments on load data at different levels, providing a reference for researchers to compare different load forecasting models.
Differential Evolution Algorithm based Hyper-Parameters Selection of Transformer Neural Network Model for Load Forecasting
We apply several metaheuristics namely Differential Evolution to find the optimal hyperparameters of the Transformer-based Neural Network to produce accurate forecasts.
DeepTSF: Codeless machine learning operations for time series forecasting
DeepTSF automates key aspects of the ML lifecycle, making it an ideal tool for data scientists and MLops engineers engaged in machine learning (ML) and deep learning (DL)-based forecasting.
Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution
The concatenating order of LSTM and SC in the proposed hybrid network provides an excellent capability of extraction of sequence-dependent features and other hierarchical spatial features.
