A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
An Effect of Machine Learning Techniques in Electrical Load forecasting and Optimization of Renewable Energy Sources
https://orcid.org/http://orcid.org/0000-0003-3643-4524
The prediction of the load from a day ahead or a week ahead is called short-term load forecasting. STLF using ANN gives better results in the power grid because the construction of the model is precise, implementation is easy and the performances are good. The weight consisted neural model is a good whose optimal value was found by using various optimization techniques. This paper explains the effect of different machine learning techniques like genetic algorithm, particle swarm optimization, autoregressive integrated moving average, empirical mode decomposition-particle swarm optimization-adaptive network-based fuzzy inference system in STLF and fuzzy logy for the optimization of renewable energy sources, i.e. solar and wind which is also used for the training of the artificial neural network with the silent effect of backpropagation. The study of different machine learning techniques presented their ability to work to produce the results and their extended application in STLF. From the simulation results, we got an empirical mode decomposition-particle swarm optimization-adaptive network-based fuzzy interference system that provides minor error, which is very much permissible compared to other techniques.
An Effect of Machine Learning Techniques in Electrical Load forecasting and Optimization of Renewable Energy Sources
https://orcid.org/http://orcid.org/0000-0003-3643-4524
The prediction of the load from a day ahead or a week ahead is called short-term load forecasting. STLF using ANN gives better results in the power grid because the construction of the model is precise, implementation is easy and the performances are good. The weight consisted neural model is a good whose optimal value was found by using various optimization techniques. This paper explains the effect of different machine learning techniques like genetic algorithm, particle swarm optimization, autoregressive integrated moving average, empirical mode decomposition-particle swarm optimization-adaptive network-based fuzzy inference system in STLF and fuzzy logy for the optimization of renewable energy sources, i.e. solar and wind which is also used for the training of the artificial neural network with the silent effect of backpropagation. The study of different machine learning techniques presented their ability to work to produce the results and their extended application in STLF. From the simulation results, we got an empirical mode decomposition-particle swarm optimization-adaptive network-based fuzzy interference system that provides minor error, which is very much permissible compared to other techniques.
An Effect of Machine Learning Techniques in Electrical Load forecasting and Optimization of Renewable Energy Sources
https://orcid.org/http://orcid.org/0000-0003-3643-4524
The prediction of the load from a day ahead or a week ahead is called short-term load forecasting. STLF using ANN gives better results in the power grid because the construction of the model is precise, implementation is easy and the performances are good. The weight consisted neural model is a good whose optimal value was found by using various optimization techniques. This paper explains the effect of different machine learning techniques like genetic algorithm, particle swarm optimization, autoregressive integrated moving average, empirical mode decomposition-particle swarm optimization-adaptive network-based fuzzy inference system in STLF and fuzzy logy for the optimization of renewable energy sources, i.e. solar and wind which is also used for the training of the artificial neural network with the silent effect of backpropagation. The study of different machine learning techniques presented their ability to work to produce the results and their extended application in STLF. From the simulation results, we got an empirical mode decomposition-particle swarm optimization-adaptive network-based fuzzy interference system that provides minor error, which is very much permissible compared to other techniques.
An Effect of Machine Learning Techniques in Electrical Load forecasting and Optimization of Renewable Energy Sources
J. Inst. Eng. India Ser. B
Panda, Saroj Kumar (author) / Ray, Papia (author)
Journal of The Institution of Engineers (India): Series B ; 103 ; 721-736
2022-06-01
16 pages
Article (Journal)
Electronic Resource
English
Machine Learning Techniques for Renewable Energy Forecasting: A Comprehensive Review
| Springer Verlag | 2022
Day Ahead Regional Electrical Load Forecasting Using ANFIS Techniques
| Springer Verlag | 2020