File Name: electrical load forecasting modeling and model construction .zip
The economic growth of every nation is highly related to its electricity infrastructure, network, and availability since electricity has become the central part of everyday life in this modern world. Hence, the global demand for electricity for residential and commercial purposes has seen an incredible increase. On the other side, electricity prices keep fluctuating over the past years and not mentioning the inadequacy in electricity generation to meet global demand.
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- Electric load forecasting: literature survey and classi®cation of methods
- Electrical Load Forecasting
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Artificial Intelligence based accurately load forecasting system to forecast short and medium-term load demands[J]. Mathematical Biosciences and Engineering, , 18 1 : Article views PDF downloads 86 Cited by 0.
Mathematical Biosciences and Engineering , , 18 1 : Mathematical Biosciences and Engineering , Volume 18 , Issue 1 : Previous Article Next Article. Research article Special Issues. Artificial Intelligence based accurately load forecasting system to forecast short and medium-term load demands.
Download PDF. An efficient management and better scheduling by the power companies are of great significance for accurate electrical load forecasting. To extract the local trends and to capture the same patterns of short, and medium forecasting time series, we proposed long short-term memory LSTM , Multilayer perceptron, and convolutional neural network CNN to learn the relationship in the time series.
These models are proposed to improve the forecasting accuracy. The models were tested based on the real-world case by conducting detailed experiments to validate their stability and practicality. To predict the next 24 hours ahead load forecasting, the lowest prediction error was obtained using LSTM with R2 0. To predict the next 72 hours ahead of load forecasting, the lowest prediction error was obtained using LSTM with R2 0.
Likewise, to predict the next one week ahead load forecasting, the lowest error was obtained using CNN with R2 0. Moreover, to predict the next one-month load forecasting, the lowest prediction error was obtained using CNN with R2 0. The results reveal that proposed methods achieved better and stable performance for predicting the short, and medium-term load forecasting.
The findings of the STLF indicate that the proposed model can be better implemented for local system planning and dispatch, while it will be more efficient for MTLF in better scheduling and maintenance operations. Related Papers:. Al-Rashid, L. Paarmann, Short-term electric load forecasting using neural network models, in: Proc.
Circuits Syst. Hong, P. Pinson, S. Fan, H. Zareipour, A. Troccoli, R. Hussain, M. Nadeem, S. Gross, F. Galiana, Short-term load forecasting, Proc. Santos, A. Martins, A. Pires, Short-term load forecasting based on ANN applied to electrical distribution substations, in: 39th Int. Power Eng. UPEC - Conf. Hossain, I. Khan, F. Un-Noor, S. Sikander, M. Sadaei, P. Lee, Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series, Energy , , — Al-Hamadi, S.
Soliman, Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model, Electr. Power Syst. Song, Y. Baek, D. Hong, G. Ranaweera, N. Hubele, G. Karady, Fuzzy logic for short term load forecasting, Int.
Power Energy Syst. He, Q. Xu, J. Wan, S. Yang, Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function, Energy , , — Raza, A.
Khosravi, A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings, Renew. Energy Rev. Velasco, C. Villezas, P. Palahang, J. Humanoid, Nanotechnology, Inf. Aguiar, L. Calavia, B. Carro, A. Buitrago, S. Asfour, Short-term forecasting of electric loads using nonlinear autoregressive Artificial Neural Networks with exogenous vector inputs, Energies , 10 , Suganthi, S. Iniyan, A. Samuel, Applications of fuzzy logic in renewable energy systems- A review, Renew.
Kim, J-K. Park, K-J. Hwang, S-H. Kim, Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems, IEEE Trans.
Niu, H. Shi, D. Wu, Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm, Appl. Soft Comput. Kaytez, M. Taplamacioglu, E. Cam, F. Hardalac, Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines, Int. Li, X. Wu, Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition, in: IEEE Int.
Williams, D. Ghofrani, M. Ghayekhloo, A. Arabali, A. Ghayekhloo, A hybrid short-term load forecasting with a new input selection framework, Energy , 81 , — Fan, L. Peng, W. Hong, Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model, Appl. Energy , , 13— Mamlook, O. Badran, E. Abdulhadi, A fuzzy inference model for short-term load forecasting, Energy Policy, 37 , — Metaxiotis, A.
Electric load forecasting: literature survey and classi®cation of methods
Artificial Intelligence based accurately load forecasting system to forecast short and medium-term load demands[J]. Mathematical Biosciences and Engineering, , 18 1 : Article views PDF downloads 86 Cited by 0. Mathematical Biosciences and Engineering , , 18 1 : Mathematical Biosciences and Engineering , Volume 18 , Issue 1 : Previous Article Next Article. Research article Special Issues.
The estimated parameters in Table are used to predict the peak load for the years from to Electric Load Modeling for Long-Term Forecasting. .
Electrical Load Forecasting
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