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[ICML 2024] A novel, efficient lightweight approach combining convolutional operations with adaptive spectral analysis as a foundation model for different time series tasks
A TensorFlow Keras implementation of "Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts" (KDD 2018)
A PyTorch Library for Multi-Task Learning
This repository holds the code and data for "Multi-task Learning Based Attentive Quantile Regression Temporal Convolutional Network for Multi-energy Probabilistic Load Forecasting"
This example walks through the process of developing an optimization routine that uses forecast pricing and loading conditions to optimally store/sell energy from a grid-scale battery system.
This repository features an Energy Optimization System (EOS) that optimizes energy distribution, usage for batteries, heat pumps& household devices. It includes predictive models for electricity pr…
NILM (Non-intrusive Load Monitoring) 实验代码与结果 本仓库包含使用深度学习模型（LSTM、RNN 和 MultiHead Transformer）进行非侵入式负荷检测的代码和实验结果。NILM 技术用于从总负荷数据中提取各个电器的用电信息，能够帮助用户和电力公司更好地进行用电管理与优化。
This is the official implementation of our research paper "An explainable unified multi-source domain adaptation framework for short-term energy load forecasting"
1) How to Prepare Time Series Data for CNNs and LSTMs?? 2) How to Develop CNNs for Time Series Forecasting?? 3) How to Develop LSTMs for Time Series Forecasting?? 4) How to Load and Explore Househo…
Python training and test data, code, and results of a DMP-PCFC model for multi-energy loads forecasting in integrated energy systems.
A multi-task learning method for multi-energy load forecasting based on synthesis correlation analysis and load participation factor
Official implementation of our research paper. DOI: 10.1109/JIOT.2024.3360882
Exploring Different Personalization Mechanisms for Federated Time Series Forecasting
Official implementation of our research paper. DOI: 10.1007/s10489-024-05856-6
ChatEV: Predicting electric vehicle charging demand as natural language processing
A real-world dataset for EV-related research, e.g., spatiotemporal prediction and urban energy management.
In PyTorch Learing Neural Networks Likes CNN、BiLSTM
Short-term wind speed forecasting model based on an attention-gated recurrent neural network and error correction strategy