Load forecasting models in smart grid using smart meter information: a review

F Dewangan, AY Abdelaziz, M Biswal - Energies, 2023‏ - mdpi.com
The smart grid concept is introduced to accelerate the operational efficiency and enhance
the reliability and sustainability of power supply by operating in self-control mode to find and …

Data analytics in the supply chain management: Review of machine learning applications in demand forecasting

A Aamer, LP Eka Yani… - Operations and Supply …, 2020‏ - journal.oscm-forum.org
In today's fast-paced global economy coupled with the availability of mobile internet and
social networks, several business models have been disrupted. This disruption brings a …

Short-term load forecasting and associated weather variables prediction using ResNet-LSTM based deep learning

X Chen, W Chen, V Dinavahi, Y Liu, J Feng - IEEE Access, 2023‏ - ieeexplore.ieee.org
Short-term load forecasting is mainly utilized in control centers to explore the changing
patterns of consumer loads and predict the load value at a certain time in the future. It is one …

A novel wavelet-based ensemble method for short-term load forecasting with hybrid neural networks and feature selection

S Li, P Wang, L Goel - IEEE Transactions on power systems, 2015‏ - ieeexplore.ieee.org
In this paper, a new ensemble forecasting model for short-term load forecasting (STLF) is
proposed based on extreme learning machine (ELM). Four important improvements are …

Small-scale building load forecast based on hybrid forecast engine

M Mohammadi, F Talebpour, E Safaee… - Neural Processing …, 2018‏ - Springer
Electricity load forecasting plays an important role for optimal power system operation.
Accordingly, short term load forecast (STLF) is also becoming an important task by …

Household electricity demand forecast based on context information and user daily schedule analysis from meter data

YH Hsiao - IEEE Transactions on Industrial Informatics, 2014‏ - ieeexplore.ieee.org
The very short-term load forecasting (VSTLF) problem is of particular interest for use in smart
grid and automated demand response applications. An effective solution for VSTLF can …

Real-time anomaly detection for very short-term load forecasting

J Luo, T Hong, M Yue - Journal of Modern Power Systems and …, 2018‏ - ieeexplore.ieee.org
Although the recent load information is critical to very short-term load forecasting (VSTLF),
power companies often have difficulties in collecting the most recent load values accurately …

A novel spatio-temporal wind power forecasting framework based on multi-output support vector machine and optimization strategy

P Lu, L Ye, W Zhong, Y Qu, B Zhai, Y Tang… - Journal of Cleaner …, 2020‏ - Elsevier
The integration of a large number of wind farms poses big challenges to the secure and
economical operation of power systems, and ultra-short-term wind power forecasting is an …

Genetic optimal regression of relevance vector machines for electricity pricing signal forecasting in smart grids

M Alamaniotis, D Bargiotas… - IEEE transactions on …, 2015‏ - ieeexplore.ieee.org
Price-directed demand in smart grids operating within deregulated electricity markets calls
for real-time forecasting of the price of electricity for the purpose of scheduling demand at the …

Interval forecasting of electricity demand: A novel bivariate EMD-based support vector regression modeling framework

T **ong, Y Bao, Z Hu - International Journal of Electrical Power & Energy …, 2014‏ - Elsevier
Highly accurate interval forecasting of electricity demand is fundamental to the success of
reducing the risk when making power system planning and operational decisions by …