Load forecasting techniques for power system: Research challenges and survey
The main and pivot part of electric companies is the load forecasting. Decision-makers and
think tank of power sectors should forecast the future need of electricity with large accuracy …
think tank of power sectors should forecast the future need of electricity with large accuracy …
Industry 4.0 and demand forecasting of the energy supply chain: A literature review
The number of publications in demand forecasting of the energy supply chain augmented
meaningfully due to the 2008 global financial crisis and its consequence on the global …
meaningfully due to the 2008 global financial crisis and its consequence on the global …
Adaptive normalization for non-stationary time series forecasting: A temporal slice perspective
Deep learning models have progressively advanced time series forecasting due to their
powerful capacity in capturing sequence dependence. Nevertheless, it is still challenging to …
powerful capacity in capturing sequence dependence. Nevertheless, it is still challenging to …
Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia
Accurate and reliable forecasting models for electricity demand (G) are critical in
engineering applications. They assist renewable and conventional energy engineers …
engineering applications. They assist renewable and conventional energy engineers …
Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple …
Real-time energy management systems that are designed to support consumer supply and
demand spectrums of electrical energy continue to face challenges with respect to designing …
demand spectrums of electrical energy continue to face challenges with respect to designing …
Investigating the potential of nuclear energy in achieving a carbon-free energy future
J Krūmiņš, M Kļaviņš - Energies, 2023 - mdpi.com
This scientific paper discusses the importance of reducing greenhouse gas emissions to
mitigate the effects of climate change. The proposed strategy is to reach net-zero emissions …
mitigate the effects of climate change. The proposed strategy is to reach net-zero emissions …
A data mining based load forecasting strategy for smart electrical grids
Smart electrical grids, which involve the application of intelligent information and
communication technologies, are becoming the core ingredient in the ongoing …
communication technologies, are becoming the core ingredient in the ongoing …
Multi-granularity residual learning with confidence estimation for time series prediction
Time-series prediction is of high practical value in a wide range of applications such as
econometrics and meteorology, where the data are commonly formed by temporal patterns …
econometrics and meteorology, where the data are commonly formed by temporal patterns …
Integration of renewable based distributed generation for distribution network expansion planning
Electrical energy is critical to a country's socioeconomic progress. Distribution system
expansion planning addresses the services that must be installed for the distribution …
expansion planning addresses the services that must be installed for the distribution …
[PDF][PDF] Electricity load forecasting in Thailand using deep learning models
The objective of this research is to improve the short-term load forecasting accuracy using
deep learning models such as long short-term memory (LSTM) and deep belief network …
deep learning models such as long short-term memory (LSTM) and deep belief network …