Review and prospect of data-driven techniques for load forecasting in integrated energy systems
With synergies among multiple energy sectors, integrated energy systems (IESs) have been
recognized lately as an effective approach to accommodate large-scale renewables and …
recognized lately as an effective approach to accommodate large-scale renewables and …
Energy forecasting: A review and outlook
Forecasting has been an essential part of the power and energy industry. Researchers and
practitioners have contributed thousands of papers on forecasting electricity demand and …
practitioners have contributed thousands of papers on forecasting electricity demand and …
Short-term load forecasting based on LSTM networks considering attention mechanism
J Lin, J Ma, J Zhu, Y Cui - International Journal of Electrical Power & Energy …, 2022 - Elsevier
Reliable and accurate zonal electricity load forecasting is essential for power system
operation and planning. Probabilistic load forecasts can present more comprehensive …
operation and planning. Probabilistic load forecasts can present more comprehensive …
Review of smart meter data analytics: Applications, methodologies, and challenges
The widespread popularity of smart meters enables an immense amount of fine-grained
electricity consumption data to be collected. Meanwhile, the deregulation of the power …
electricity consumption data to be collected. Meanwhile, the deregulation of the power …
Probabilistic individual load forecasting using pinball loss guided LSTM
The installation of smart meters enables the collection of massive fine-grained electricity
consumption data and makes individual consumer level load forecasting possible …
consumption data and makes individual consumer level load forecasting possible …
Gaining insight into solar photovoltaic power generation forecasting utilizing explainable artificial intelligence tools
Over the last two decades, Artificial Intelligence (AI) approaches have been applied to
various applications of the smart grid, such as demand response, predictive maintenance …
various applications of the smart grid, such as demand response, predictive maintenance …
The future of forecasting for renewable energy
Forecasting for wind and solar renewable energy is becoming more important as the amount
of energy generated from these sources increases. Forecast skill is improving, but so too is …
of energy generated from these sources increases. Forecast skill is improving, but so too is …
Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation
Accurate customer baseline load (CBL) estimation is critical for implementing incentive-
based demand response (DR) programs. The increasing penetration of grid-tied distributed …
based demand response (DR) programs. The increasing penetration of grid-tied distributed …
Using Bayesian deep learning to capture uncertainty for residential net load forecasting
Decarbonization of electricity systems drives significant and continued investments in
distributed energy sources to support the cost-effective transition to low-carbon energy …
distributed energy sources to support the cost-effective transition to low-carbon energy …
Deep learning-based multivariate probabilistic forecasting for short-term scheduling in power markets
JF Toubeau, J Bottieau, F Vallée… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
In the current competition framework governing the electricity sector, complex dependencies
exist between electrical and market data, which complicates the decision-making procedure …
exist between electrical and market data, which complicates the decision-making procedure …