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Load forecasting models in smart grid using smart meter information: a review
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 …
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
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 …
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
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 …
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
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 …
proposed based on extreme learning machine (ELM). Four important improvements are …
Small-scale building load forecast based on hybrid forecast engine
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 …
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
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 …
grid and automated demand response applications. An effective solution for VSTLF can …
Real-time anomaly detection for very short-term load forecasting
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 …
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
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 …
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
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 …
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
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 …
reducing the risk when making power system planning and operational decisions by …