A comparative analysis of artificial neural network architectures for building energy consumption forecasting
Smart grids have recently attracted increasing attention because of their reliability, flexibility,
sustainability, and efficiency. A typical smart grid consists of diverse components such as …
sustainability, and efficiency. A typical smart grid consists of diverse components such as …
Development and evaluation of the combined machine learning models for the prediction of dam inflow
Predicting dam inflow is necessary for effective water management. This study created
machine learning algorithms to predict the amount of inflow into the Soyang River Dam in …
machine learning algorithms to predict the amount of inflow into the Soyang River Dam in …
Evaluation of rainfall erosivity factor estimation using machine and deep learning models
Rainfall erosivity factor (R-factor) is one of the Universal Soil Loss Equation (USLE) input
parameters that account for impacts of rainfall intensity in estimating soil loss. Although …
parameters that account for impacts of rainfall intensity in estimating soil loss. Although …
Determining the tiers of a supply chain using machine learning algorithms
KJ Park - Symmetry, 2021 - mdpi.com
Companies in the same supply chain influence each other, so sharing information enables
more efficient supply chain management. An efficient supply chain must have a symmetry of …
more efficient supply chain management. An efficient supply chain must have a symmetry of …
[HTML][HTML] Comparison of machine learning algorithms for discharge prediction of multipurpose dam
For effective water management in the downstream area of a dam, it is necessary to estimate
the amount of discharge from the dam to quantify the flow downstream of the dam. In this …
the amount of discharge from the dam to quantify the flow downstream of the dam. In this …
Residential electrical load forecasting based on a real-time evidential time series prediction method
Load forecasting is essential for efficient microgrid management, providing key advantages
in operational efficiency, cost control, and grid reliability. As microgrids become increasingly …
in operational efficiency, cost control, and grid reliability. As microgrids become increasingly …
A study on the development of machine-learning based load transfer detection algorithm for distribution planning
JH Kim, BS Lee, CH Kim - Energies, 2020 - mdpi.com
Distribution planning refers to the act of estimating the risks of distribution systems that may
arise in the future and establishing investment plans to cope with them. Forecasted loads …
arise in the future and establishing investment plans to cope with them. Forecasted loads …
Forecasting of Iron Ore Prices using Machine Learning
WC Lee, YS Kim, JM Kim, CK Lee - Journal of the Korea Industrial …, 2020 - koreascience.kr
The price of iron ore has continued to fluctuate with high demand and supply from many
countries and companies. In this business environment, forecasting the price of iron ore has …
countries and companies. In this business environment, forecasting the price of iron ore has …
Electric power demand prediction using deep learning model with temperature data
HS Yoon, SB Jeong - KIPS transactions on software and data …, 2022 - koreascience.kr
Recently, researches using deep learning-based models are being actively conducted to
replace statistical-based time series forecast techniques to predict electric power demand …
replace statistical-based time series forecast techniques to predict electric power demand …