Short-term energy forecasting using machine-learning-based ensemble voting regression
Meeting the required amount of energy between supply and demand is indispensable for
energy manufacturers. Accordingly, electric industries have paid attention to short-term …
energy manufacturers. Accordingly, electric industries have paid attention to short-term …
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 …
Gaussian process regression‐based load forecasting model
Abstract In this paper, Gaussian Process Regression (GPR)‐based models which use the
Bayesian approach to regression analysis problem such as load forecasting (LF) are …
Bayesian approach to regression analysis problem such as load forecasting (LF) are …
Optimal planning of electric vehicle battery centralized charging station based on EV load forecasting
This article studies the planning of swapping electric vehicle (SEV) battery centralized
charging station (BCCS) based on EV spatial-temporal load forecasting. First, according to …
charging station (BCCS) based on EV spatial-temporal load forecasting. First, according to …
Deep learning based relay for online fault detection, classification, and fault location in a grid-connected microgrid
In this article, a maiden attempt have been taken for the online detection of faults,
classification of faults, and identification of the fault locations of a grid-connected Micro-grid …
classification of faults, and identification of the fault locations of a grid-connected Micro-grid …
Daily load forecasting based on a combination of classification and regression tree and deep belief network
PP Phyo, C Jeenanunta - IEEE Access, 2021 - ieeexplore.ieee.org
The next-day load forecasting is complex due to the load pattern variations driven by
external factors, such as weather and time. This study proposes a hybrid model that …
external factors, such as weather and time. This study proposes a hybrid model that …
Hybrid ensemble deep learning-based approach for time series energy prediction
The energy manufacturers are required to produce an accurate amount of energy by
meeting the energy requirements at the end-user side. Consequently, energy prediction …
meeting the energy requirements at the end-user side. Consequently, energy prediction …
Improved GRU prediction of paper pulp press variables using different pre-processing methods
Predictive maintenance strategies are becoming increasingly more important with the
increased needs for automation and digitalization within pulp and paper manufacturing …
increased needs for automation and digitalization within pulp and paper manufacturing …
[PDF][PDF] Long-term solar irradiance forecasting using multilinear predictors
As the demand for crude oil is increasing every day, prices and pollution are both increasing
in return, which has harmful effects on the environment. Thus, more attempts are being …
in return, which has harmful effects on the environment. Thus, more attempts are being …
Advanced ml-based ensemble and deep learning models for short-term load forecasting: Comparative analysis using feature engineering
PP Phyo, C Jeenanunta - Applied Sciences, 2022 - mdpi.com
Short-term load forecasting (STLF) plays a pivotal role in the electricity industry because it
helps reduce, generate, and operate costs by balancing supply and demand. Recently, the …
helps reduce, generate, and operate costs by balancing supply and demand. Recently, the …