A NOVEL APPROACH FOR SHORT-TERM LOAD FORECASTING USING SUPPORT VECTOR MACHINES

2004 ◽  
Vol 14 (05) ◽  
pp. 329-335 ◽  
Author(s):  
LIANG TIAN ◽  
AFZEL NOORE
Keyword(s):  
Neural Network ◽  
Load Forecasting ◽  
Support Vector ◽  
Short Term ◽  
Feed Forward Neural Network ◽  
Modeling Approach ◽  
Novel Approach ◽  
Power Load ◽  
Short Term Load Forecasting ◽  
Network Approaches

A support vector machine (SVM) modeling approach for short-term load forecasting is proposed. The SVM learning scheme is applied to the power load data, forcing the network to learn the inherent internal temporal property of power load sequence. We also study the performance when other related input variables such as temperature and humidity are considered. The performance of our proposed SVM modeling approach has been tested and compared with feed-forward neural network and cosine radial basis function neural network approaches. Numerical results show that the SVM approach yields better generalization capability and lower prediction error compared to those neural network approaches.

scholarly journals Short-Term Load Forecasting with Tensor Partial Least Squares-Neural Network

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 990 ◽  
Author(s):  
Yu Feng ◽  
Xianfeng Xu ◽  
Yun Meng
Keyword(s):  
Neural Network ◽  
Least Squares ◽  
Partial Least Squares ◽  
Latent Variables ◽  
Load Forecasting ◽  
Support Vector ◽  
Short Term ◽  
Inner Model ◽  
Power Load ◽  
Short Term Load Forecasting

Short-term load forecasting is very important for power systems. The load is related to many factors which compose tensors. However, tensors cannot be input directly into most traditional forecasting models. This paper proposes a tensor partial least squares-neural network model (TPN) to forecast the power load. The model contains a tensor decomposition outer model and a nonlinear inner model. The outer model extracts common latent variables of tensor input and vector output and makes the residuals less than the threshold by iteration. The inner model determines the relationship between the latent variable matrix and the output by using a neural network. This model structure can preserve the information of tensors and the nonlinear features of the system. Three classical models, partial least squares (PLS), least squares support vector machine (LSSVM) and neural network (NN), are selected to compare the forecasting results. The results show that the proposed model is efficient for short-term load and daily load peak forecasting. Compared to PLS, LSSVM and NN, the TPN has the best forecasting accuracy.

Comparative Study Of Short Term Load Forecasting Using Multilayer Feed Forward Neural Network With Back Propagation Learning And Radial Basis Functional Neural Network

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Amit Tiwari ◽  
Adarsh Dhar Dubey ◽  
And Devesh Patel
Keyword(s):  
Neural Network ◽  
Power System ◽  
Structural Changes ◽  
Unit Commitment ◽  
Network Reliability ◽  
Peak Load ◽  
Load Forecasting ◽  
Short Term ◽  
Feed Forward Neural Network ◽  
Short Term Load Forecasting

The term load forecast refers to the projected load requirement using systematic process of defining load in sufficient quantitative detail so that important power system expansion decisions can be made. Load forecasting is necessary for economic generation of power, economic allocation between plants (unit commitment scheduling), maintenance scheduling & for system security such as peak load shaving by power interchange with interconnected utilities. With structural changes to electricity in recent years, there is an emphasis on Short Term Load Forecasting (STLF).STLF is the essential part of power system planning & operation. Basic operating functions such as unit commitment, economic dispatch, and fuel scheduling & unit maintenance can be performed efficiently with an accurate forecast. Short term load forecasting can help to estimate load flows & to make decisions that can prevent overloading. Timely implementations of such decisions lead to improvement of network reliability & to the reduced occurrences of equipment failures & blackouts. The aim of short term load forecasting is to predict future electricity demands based, traditionally on historical data and predicted weather conditions. Short term load forecasting in its basic form is a statistical problem, where in the previous load values (time series variables) and influencing factors (casual variables) are used to determine the future loads.

scholarly journals Short Term load forecasting for a Captive Power Plant Using Artificial Neural Network

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Air Temperature ◽  
Power Plants ◽  
Load Forecasting ◽  
Uttar Pradesh ◽  
Ambient Air ◽  
Short Term ◽  
Feed Forward Neural Network ◽  
Marquardt Algorithm ◽  
Short Term Load Forecasting

The irregularity of Indian grid system increases, with increase in the power demand. The quality of power supplied by the power grid is also poor due to continuous variation in frequency and voltage. To overcome this problem of power deficit, Captive Power Plants installed capacity has grown at a faster rate. Here short term load forecasting of Yara Fertilizers India Private limited installed at Babrala, Uttar Pradesh is performed using multi-layer feed-forward Neural network in MATLAB. The algorithm used is a Levenberg Marquardt algorithm. However, the training and results from ANN are very fast and accurate. Inputs given to the Neural Network are time, ambient air temperature from the compressor, cool air temperature at the compressor and IGV opening. The need, benefits and growth of CPP in India and use of ANN for short term load forecasting of CPP has been explained in detail in the paper.

A Short-Term Load Forecasting Method of Warship Based on PSO-SVM Method

2011 ◽  
Vol 127 ◽  
pp. 569-574
Author(s):  
Dong Liang Li ◽  
Xiao Feng Zhang ◽  
Ming Zhong Qiao ◽  
Gang Cheng
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Load Forecasting ◽  
Support Vector ◽  
Short Term ◽  
Swarm Optimization ◽  
Power Load ◽  
Forecasting Method ◽  
Short Term Load Forecasting ◽  
Adaptive Pso

The power load characteristics of warship on a specific task was analyzed,and a task-based forecasting method for warship short-term load forecasting was presented. the new influencing factors of warship power load were used in modeling which is different with the land grid and civilian vessels grid. Theory of particle swarm optimization and Support vector machine was disscused first, and the method of particle swarm optimization was improved to have the ability of adaptive parameter optimization. and the method of support vector machine was improved by the adaptive PSO optimizational method. then a new adaptive short-term load forecasting model was established by the adaptive PSO-SVM method. finally Through simulation results show that the adaptive PSO-SVM method is highly feasible to predict with high accuracy and high generalization capability.

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.

An Improved Short-Term Load Forecasting Method of Warship

2011 ◽  
Vol 127 ◽  
pp. 575-581
Author(s):  
Dong Liang Li ◽  
Xiao Feng Zhang ◽  
Ming Zhong Qiao ◽  
Gang Cheng
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Load Forecasting ◽  
Support Vector ◽  
Short Term ◽  
Swarm Optimization ◽  
Power Load ◽  
Forecasting Method ◽  
Short Term Load Forecasting ◽  
Adaptive Pso

The power load characteristics of warship on a specific task was analyzed,and a task-based forecasting method for warship short-term load forecasting was presented. the new influencing factors of warship power load were used in modeling which is different with the land grid and civilian vessels grid. Theory of particle swarm optimization and Support vector machine was disscused first, and the method of particle swarm optimization was improved to have the ability of adaptive parameter optimization. and the method of support vector machine was improved by the adaptive PSO optimizational method. then a new adaptive short-term load forecasting model was established by the adaptive PSO-SVM method. finally Through simulation results show that the adaptive PSO-SVM method is highly feasible to predict with high accuracy and high generalization capability.

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