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Found 7 papers, 1 papers with code
Data-driven building energy efficiency prediction using physics-informed neural networks
On top of this neural network, a function, based on physics equations, calculates the energy consumption of the building based on heat losses and enhances the loss function of the deep learning model.
Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time series
Short-term load forecasting (STLF) is crucial for the daily operation of power grids.
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.
Calibration of Transformer-based Models for Identifying Stress and Depression in Social Media
To resolve the above issues, we present the first study in the task of depression and stress detection in social media, which injects extra linguistic information in transformer-based models, namely BERT and MentalBERT.
Targeted demand response for flexible energy communities using clustering techniques
The k-means algorithm with dynamic time warping distance considering 14 clusters exhibits the highest performance with a PPS of 0. 689.
In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performance
In power grids, short-term load forecasting (STLF) is crucial as it contributes to the optimization of their reliability, emissions, and costs, while it enables the participation of energy companies in the energy market.
A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers
Short-term load forecasting (STLF) is vital for the effective and economic operation of power grids and energy markets.
