@Article{cmc.2022.032971,
AUTHOR = {Manish Kumar, Nitai Pal},
TITLE = {Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {74},
YEAR = {2023},
NUMBER = {3},
PAGES = {4785--4799},
URL = {http://www.techscience.com/cmc/v74n3/50985},
ISSN = {1546-2226},
ABSTRACT = {Increasing energy demands due to factors such as population,
globalization, and industrialization has led to increased challenges for existing
energy infrastructure. Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these
challenges with reliable, cheap, and easily available sources of energy. Grid
integration of renewable energy and other clean distributed generation is
increasing continuously to reduce carbon and other air pollutants emissions.
But the integration of distributed energy sources and increase in electric
demand enhance instability in the grid. Short-term electrical load forecasting
reduces the grid fluctuation and enhances the robustness and power quality
of the grid. Electrical load forecasting in advance on the basic historical data
modelling plays a crucial role in peak electrical demand control, reinforcement
of the grid demand, and generation balancing with cost reduction. But
accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data. Machine learning and artificial
intelligence have recognized more accurate and reliable load forecasting methods based on historical load data. The purpose of this study is to model the
electrical load of Jajpur, Orissa Grid for forecasting of load using regression
type machine learning algorithms Gaussian process regression (GPR). The
historical electrical data and whether data of Jajpur is taken for modelling
and simulation and the data is decided in such a way that the model will be
considered to learn the connection among past, current, and future dependent
variables, factors, and the relationship among data. Based on this modelling
of data the network will be able to forecast the peak load of the electric grid
one day ahead. The study is very helpful in grid stability and peak load control
management.},
DOI = {10.32604/cmc.2022.032971}
}