scholarly journals A New Neural Network Approach to Short Term Load Forecasting of Electrical Power Systems

Energies ◽  
2011 ◽  
Vol 4 (3) ◽  
pp. 488-503 ◽  
Author(s):  
Nima Amjady ◽  
Farshid Keynia
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Electrical Power ◽  
Load Forecasting ◽  
Network Approach ◽  
Short Term ◽  
Electrical Power Systems ◽  
Neural Network Approach ◽  
Short Term Load Forecasting

Mathematical Programming Based Short Term Load Forecasting Algorithm, Case Study: Turkey 2010

2012 ◽  
Vol 433-440 ◽  
pp. 3934-3938
Author(s):  
Nurettin Çetinkaya
Keyword(s):  
Mathematical Programming ◽  
Power Systems ◽  
Computational Algorithm ◽  
Electrical Power ◽  
Load Forecasting ◽  
Short Term ◽  
Electrical Power Systems ◽  
Short Term Load Forecasting ◽  
The Mean

Short-term load forecasting (STLF) is an important problem in the operation of electrical power generation and transmission. In this paper, STLF algorithm was developed for electrical power systems using mathematical programming with Matlab. A fast and efficient computational algorithm has been obtained for STLF. The mean absolute percentage errors (MAPE) of daily loads forecast and weekly loads forecast for Turkey are found as 1,76%, 1,92%, respectively.

Short-Term Load Forecasting Using Hybrid Neural Network

2021 ◽  
Vol 12 (1) ◽  
pp. 142-156
Author(s):  
Muhammad Nadeem ◽  
Muhammad Altaf ◽  
Ayaz Ahmad
Keyword(s):  
Neural Network ◽  
New England ◽  
Low Cost ◽  
Electrical Power ◽  
Load Forecasting ◽  
Electricity Consumption ◽  
Forecast Model ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Multiple Variables

One of the important factors in generating low cost electrical power is the accurate forecasting of electricity consumption called load forecasting. The major objective of the load forecasting is to trim down the error between actual load and forecasted load. Due to the nonlinear nature of load forecasting and its dependency on multiple variables, the traditional forecasting methods are normally outperformed by artificial intelligence techniques. In this research paper, a robust short term load forecasting technique for one to seven days ahead is introduced based on particle swarm optimization (PSO) and Levenberg Marquardt (LM) neural network forecast model, where the PSO and LM algorithm are used for the training process of neural network. The proposed methods are tested to predict the load of the New England Power Pool region's grid and compared with the existing techniques using mean absolute percentage errors to analyze the performance of the proposed methods. Forecast results confirm that the proposed LM and PSO-based neural network schemes outperformed the existing techniques.

scholarly journals Short-Term Load Forecasting Based on Deep Learning Bidirectional LSTM Neural Network

2021 ◽  
Vol 11 (17) ◽  
pp. 8129 ◽  
Author(s):  
Changchun Cai ◽  
Yuan Tao ◽  
Tianqi Zhu ◽  
Zhixiang Deng
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Power Systems ◽  
Network Architecture ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Short Term ◽  
Architecture Model ◽  
Power Load ◽  
Short Term Load Forecasting

Accurate load forecasting guarantees the stable and economic operation of power systems. With the increasing integration of distributed generations and electrical vehicles, the variability and randomness characteristics of individual loads and the distributed generation has increased the complexity of power loads in power systems. Hence, accurate and robust load forecasting results are becoming increasingly important in modern power systems. The paper presents a multi-layer stacked bidirectional long short-term memory (LSTM)-based short-term load forecasting framework; the method includes neural network architecture, model training, and bootstrapping. In the proposed method, reverse computing is combined with forward computing, and a feedback calculation mechanism is designed to solve the coupling of before and after time-series information of the power load. In order to improve the convergence of the algorithm, deep learning training is introduced to mine the correlation between historical loads, and the multi-layer stacked style of the network is established to manage the power load information. Finally, actual data are applied to test the proposed method, and a comparison of the results of the proposed method with different methods shows that the proposed method can extract dynamic features from the data as well as make accurate predictions, and the availability of the proposed method is verified with real operational data.

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