Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review
Meteorological changes urge engineering communities to look for sustainable and clean
energy technologies to keep the environment safe by reducing CO 2 emissions. The …
energy technologies to keep the environment safe by reducing CO 2 emissions. The …
Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model
R Rathipriya, AA Abdul Rahman… - Neural Computing and …, 2023 - Springer
Demand forecasting is a scientific and methodical assessment of future demand for a critical
product. The effective Demand Forecast Model (DFM) enables pharmaceutical companies to …
product. The effective Demand Forecast Model (DFM) enables pharmaceutical companies to …
District heating load forecasting with a hybrid model based on LightGBM and FB-prophet
A Shakeel, D Chong, J Wang - Journal of Cleaner Production, 2023 - Elsevier
Accurate short-term load forecasting for the district heating network (DHN) is vital for energy
companies to develop a stable and dependable generation capacity. Many machine …
companies to develop a stable and dependable generation capacity. Many machine …
Short-term district power load self-prediction based on improved XGBoost model
W Cao, Y Liu, H Mei, H Shang, Y Yu - Engineering Applications of Artificial …, 2023 - Elsevier
Distributed generation and diversified loads increase the uncertainty of district power
prediction. Useful prediction requires a highly accurate model, and there are several …
prediction. Useful prediction requires a highly accurate model, and there are several …
Energy demand forecasting and optimizing electric systems for developing countries
Currently, developing countries are experiencing a massive shift toward industrialization.
Developing countries lack the technical sophistication and infrastructure to encourage low …
Developing countries lack the technical sophistication and infrastructure to encourage low …
Multi-step-ahead electricity load forecasting using a novel hybrid architecture with decomposition-based error correction strategy
D Wang, C Yue, A ElAmraoui - Chaos, Solitons & Fractals, 2021 - Elsevier
In this study, a novel architecture combining a hybrid learning paradigm and an error
correction strategy is presented for multi-step-ahead electricity load forecasting. The detail of …
correction strategy is presented for multi-step-ahead electricity load forecasting. The detail of …
Novel wind-speed prediction system based on dimensionality reduction and nonlinear weighting strategy for point-interval prediction
X Wang, J Wang, X Niu, C Wu - Expert Systems with Applications, 2024 - Elsevier
In the context of today's energy shortage, wind energy plays a crucial role as one of the most
widely used renewable energy sources. However, in order to fully utilize the potential of …
widely used renewable energy sources. However, in order to fully utilize the potential of …
Long-term energy and peak power demand forecasting based on sequential-XGBoost
Long-term energy and peak power forecast are essential tasks for the effective planning of
power systems. Utilities often conduct long-term energy consumption and peak power …
power systems. Utilities often conduct long-term energy consumption and peak power …
Deterioration of electrical load forecasting models in a smart grid environment
Smart Grid (SG) is a digitally enabled power grid with an automatic capability to control
electricity and information between utility and consumer. SG data streams are heterogenous …
electricity and information between utility and consumer. SG data streams are heterogenous …
[HTML][HTML] Sparse dynamic graph learning for district heat load forecasting
Accurate heat load forecasting is crucial for the efficient operation and management of
district heating systems. This study introduces a novel Sparse Dynamic Graph Neural …
district heating systems. This study introduces a novel Sparse Dynamic Graph Neural …