An enhanced monthly runoff time series prediction using extreme learning machine optimized by salp swarm algorithm based on time varying filtering based empirical …
Reliable runoff prediction plays a significant role in reservoir scheduling, water resources
management, and efficient utilization of water resources. To effectively enhance the …
management, and efficient utilization of water resources. To effectively enhance the …
[HTML][HTML] A combination forecasting model of wind speed based on decomposition
Z Tian, H Li, F Li - Energy Reports, 2021 - Elsevier
Due to the intermittent, fluctuating and random characteristics of wind system, the output of
wind power will become unstable with the change of wind, which brings severe challenges …
wind power will become unstable with the change of wind, which brings severe challenges …
Electricity demand error corrections with attention bi-directional neural networks
Reliable forecast of electricity demand is crucial to stability, supply, and management of
electricity grids. Short-term hourly and sub-hourly demand forecasts are difficult due to the …
electricity grids. Short-term hourly and sub-hourly demand forecasts are difficult due to the …
[HTML][HTML] Hyperparameter Tuning of Load-Forecasting Models Using Metaheuristic Optimization Algorithms—A Systematic Review
Load forecasting is an integral part of the power industries. Load-forecasting techniques
should minimize the percentage error while prediction future demand. This will inherently …
should minimize the percentage error while prediction future demand. This will inherently …
Forecasting electricity demand in Turkey using optimization and machine learning algorithms
M Saglam, C Spataru, OA Karaman - Energies, 2023 - mdpi.com
Medium Neural Networks (MNN), Whale Optimization Algorithm (WAO), and Support Vector
Machine (SVM) methods are frequently used in the literature for estimating electricity …
Machine (SVM) methods are frequently used in the literature for estimating electricity …
[HTML][HTML] Universities power energy management: A novel hybrid model based on iCEEMDAN and Bayesian optimized LSTM
Rapid growth and development around the world will lead to a gradual increase in electricity
consumption. At present, colleges and universities have become the primary unit of daily …
consumption. At present, colleges and universities have become the primary unit of daily …
Prediction and evaluation of electricity price in restructured power systems using Gaussian process time series modeling
A Dejamkhooy, A Ahmadpour - Smart Cities, 2022 - mdpi.com
The electricity market is particularly complex due to the different arrangements and
structures of its participants. If the energy price in this market presents in a conceptual and …
structures of its participants. If the energy price in this market presents in a conceptual and …
Stock price index forecasting using a multiscale modelling strategy based on frequency components analysis and intelligent optimization
R Li, T Han, X Song - Applied Soft Computing, 2022 - Elsevier
The interaction and uncertainty of stock market is exactly critical for traders and investors.
Stock price prediction is a hot topic of research due to the returns and risks that coexist in …
Stock price prediction is a hot topic of research due to the returns and risks that coexist in …
A novel coupled optimization prediction model for air quality
Q Shao, J Chen, T Jiang - IEEE Access, 2023 - ieeexplore.ieee.org
PM2. 5 is a significant pollutant that negatively affects atmospheric environmental
sustainability, and accurate prediction of its concentration is crucial. Most existing prediction …
sustainability, and accurate prediction of its concentration is crucial. Most existing prediction …
基于补充集合经验模态分解的短期负荷预测模型
杨维熙， 刘勇， 舒勤 - 电网技术, 2022 - epjournal.csee.org.cn
电力负荷预测关乎电量调配和系统运行. 针对短期负荷预测, 采用补充集合经验模态分解(
complementary ensemble empirical mode decomposition, CEEMD) 算法 …
complementary ensemble empirical mode decomposition, CEEMD) 算法 …