Neural network based optimization approach for energy demand prediction in smart grid-Reference-Cited by-同舟云学术

Neural network based optimization approach for energy demand prediction in smart grid

Published:2018-01 Issue: Volume:273 Page:199-208
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Muralitharan K.,Sakthivel R.,Vishnuvarthan R.

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference48 articles.

1. A demand side management based simulation platform incorporating heuristic optimization for management of household appliances;Gudi;Int. J. Electr. Power Energy Syst.,2012

2. Multiobjective optimization technique for demand side management with load balancing approach in smart grid;Muralitharan;Neurocomputing,2016

3. QoS differential scheduling in cognitive-radio-based smart grid networks: an adaptive dynamic programming approach;Yu;EEE Trans. Neural Netw. Learn. Syst.,2016

4. Dual battery management for renewable energy integration in EV charging stations;Lee;Neurocomputing,2015

5. A hybrid method for load forecasting in smart grid based on neural networks and cuckoo search optimization approach;Lajevardy;Int. J. Renew. Energy Resour.,2015

Cited by 169 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue;Energies;2024-01-20

2. Ensemble Machine Learning Approaches for Prediction of Türkiye’s Energy Demand;Energies;2023-12-22

3. Reinforcement Learning and Stochastic Optimization with Deep Learning-Based Forecasting on Power Grid Scheduling;Processes;2023-11-08

4. Cluster-aware attentive convolutional recurrent network for multivariate time-series forecasting;Neurocomputing;2023-11

5. Short-term load forecasting for microgrid energy management system using hybrid SPM-LSTM;Sustainable Cities and Society;2023-11