scholarly journals Application of SVM methods for mid-term load forecasting

2011 ◽  
Vol 8 (1) ◽  
pp. 73-83 ◽  
Author(s):  
Milos Bozic ◽  
Milos Stojanovic
Keyword(s):  
Load Forecasting ◽  
Real Life ◽  
Support Vector ◽  
Medium Term ◽  
Training Set ◽  
Load Demand ◽  
Svm Model ◽  
Vector Machines ◽  
Distribution Company ◽  
Daily Load

This paper presents an approach for the medium-term load forecasting using Support Vector Machines (SVMs). The proposed SVM model was employed to predict the maximum daily load demand for the period of a month. Analyses of available data were performed and the most important features for the construction of SVM model are selected. It was shown that the size and the structure of the training set may significantly affect the accuracy of predictions. The presented model was tested by applying it on real-life load data obtained from distribution company 'ED Jugoistok' for the territory of city Nis and its surroundings. Experimental results show that the proposed approach gives acceptable results for the entire period of prediction, which are in range with other solutions in this area.

scholarly journals An Accurate Medium-Term Load Forecasting based on Hybrid Technique

2018 ◽  
Vol 12 (1) ◽  
pp. 161
Author(s):  
Z.M. Yasin ◽  
N.F.A. Aziz ◽  
N.A. Salim ◽  
N.A. Wahab ◽  
N.A. Rahmat
Keyword(s):  
Support Vector Machine ◽  
Load Forecasting ◽  
Least Square ◽  
Percentage Error ◽  
Support Vector ◽  
Hybrid Technique ◽  
Medium Term ◽  
Load Demand ◽  
Ant Lion Optimizer ◽  
Ant Lion

An accurate medium term load forecasting is significant for power generation scheduling, economic and reliable operation in power system. Most of classical approach for medium term load forecasting only consider total daily load demand. This approach may not provide accurate results since the load demand is fluctuated in a day. In this paper, a hybrid Ant-Lion Optimizer Least-square Support Vector Machine (ALO-LSSVM) is proposed to forecast 24-hour load demand for the next year. Ant-Lion Optimizer (ALO) is utilized to optimize the RBF Kernel parameters in Least-Square Support Vector Machine (LS-SVM). The objective of the optimization is to minimize the Mean Absolute Percentage Error (MAPE). The performance of ALO-LSSVM technique was compared with those obtained from LS-SVM technique through a 10-fold cross-validation procedure. The historical hourly load data are analyzed and appropriate features are selected for the model. There are 24 inputs and 24 outputs vectors for this model which represents 24-hour load demand for whole year. The results revealed that the high accuracy of prediction could be achieved using ALO-LSSVM.

scholarly journals Medium-Term Regional Electricity Load Forecasting through Machine Learning and Deep Learning

Designs ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 27
Author(s):  
Navid Shirzadi ◽  
Ameer Nizami ◽  
Mohammadali Khazen ◽  
Mazdak Nik-Bakht
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Smart Grids ◽  
Load Forecasting ◽  
Percentage Error ◽  
Support Vector ◽  
Medium Term ◽  
Load Demand ◽  
Electricity Load ◽  
Electricity Load Forecasting

Due to severe climate change impact on electricity consumption, as well as new trends in smart grids (such as the use of renewable resources and the advent of prosumers and energy commons), medium-term and long-term electricity load forecasting has become a crucial need. Such forecasts are necessary to support the plans and decisions related to the capacity evaluation of centralized and decentralized power generation systems, demand response strategies, and controlling the operation. To address this problem, the main objective of this study is to develop and compare precise district level models for predicting the electrical load demand based on machine learning techniques including support vector machine (SVM) and Random Forest (RF), and deep learning methods such as non-linear auto-regressive exogenous (NARX) neural network and recurrent neural networks (Long Short-Term Memory—LSTM). A dataset including nine years of historical load demand for Bruce County, Ontario, Canada, fused with the climatic information (temperature and wind speed) are used to train the models after completing the preprocessing and cleaning stages. The results show that by employing deep learning, the model could predict the load demand more accurately than SVM and RF, with an R-Squared of about 0.93–0.96 and Mean Absolute Percentage Error (MAPE) of about 4–10%. The model can be used not only by the municipalities as well as utility companies and power distributors in the management and expansion of electricity grids; but also by the households to make decisions on the adoption of home- and district-scale renewable energy technologies.

scholarly journals Mid-term load forecasting using recursive time series prediction strategy with support vector machines

2010 ◽  
Vol 23 (3) ◽  
pp. 287-298 ◽  
Author(s):  
Milos Stojanovic ◽  
Milos Bozic ◽  
Milena Stankovic
Keyword(s):  
Time Series ◽  
Support Vector Machines ◽  
Time Series Prediction ◽  
Load Forecasting ◽  
Support Vector ◽  
Load Demand ◽  
Vector Machines ◽  
Electricity Load ◽  
One Year ◽  
Prediction Strategy

Mediumterm load forecasting, using recursive time - series prediction strategy with Support Vector Machines (SVMs) is presented in this paper. The forecasting is performed for electrical maximum daily load for the period of one month. The data considered for forecasting consist of half hour daily loads and daily average temperatures for period of one year. An analysis of available data was performed and the most adequate set of features for our model are chosen. For evaluation of prediction accuracy we used data obtained from electricity load forecasting competition on the EUNITE network. Some drawn conclusions from the results are that the temperature significantly affects on load demand, but absence of future temperature information can be overcome with time - series concept. Also, it was shown that size and structure of the training set for SVM may significantly affect the accuracy of load forecasting.

New Multi-View Classification Method with Uncertain Data

2021 ◽  
Vol 16 (1) ◽  
pp. 1-23
Author(s):  
Bo Liu ◽  
Haowen Zhong ◽  
Yanshan Xiao
Keyword(s):  
Learning Strategy ◽  
State Of The Art ◽  
Uncertain Data ◽  
Real Life ◽  
Support Vector ◽  
Classification Methods ◽  
Complementary Information ◽  
Novel Approach ◽  
Svm Model ◽  
Iterative Framework

Multi-view classification aims at designing a multi-view learning strategy to train a classifier from multi-view data, which are easily collected in practice. Most of the existing works focus on multi-view classification by assuming the multi-view data are collected with precise information. However, we always collect the uncertain multi-view data due to the collection process is corrupted with noise in real-life application. In this case, this article proposes a novel approach, called uncertain multi-view learning with support vector machine (UMV-SVM) to cope with the problem of multi-view learning with uncertain data. The method first enforces the agreement among all the views to seek complementary information of multi-view data and takes the uncertainty of the multi-view data into consideration by modeling reachability area of the noise. Then it proposes an iterative framework to solve the proposed UMV-SVM model such that we can obtain the multi-view classifier for prediction. Extensive experiments on real-life datasets have shown that the proposed UMV-SVM can achieve a better performance for uncertain multi-view classification in comparison to the state-of-the-art multi-view classification methods.

scholarly journals Epileptic Seizure Prediction over EEG Data using Hybrid CNN-SVM Model with Edge Computing Services

2018 ◽  
Vol 210 ◽  
pp. 03016 ◽  
Author(s):  
Punjal Agarwal ◽  
Hwang-Cheng Wang ◽  
Kathiravan Srinivasan
Keyword(s):  
Epileptic Seizure ◽  
Life Support ◽  
Real Life ◽  
Prediction Method ◽  
Edge Computing ◽  
Support Vector ◽  
Seizure Prediction ◽  
Computing Services ◽  
Svm Model ◽  
Epileptic Seizure Prediction

Epilepsy is one of the most common neurological disorders, which is characterized by unpredictable brain seizure. About 30% of the patients are not even aware that they have epilepsy and many have to undergo surgeries to relieve the pain. Therefore, developing a robust brain-computer interface for seizure prediction can help epileptic patients significantly. In this paper, we propose a hybrid CNN-SVM model for better epileptic seizure prediction. A convolutional neural network (CNN) consists of a multilayer structure, which can be adapted and modified according to the requirement of different applications. A support vector machine is a discriminative classifier which can be described by a separating optimal hyperplane used for categorizing new samples. The combination of CNN and SVM is found to provide an effective way for epileptic prediction. Furthermore, the resulting model is made autonomous using edge computing services and is shown to be a viable seizure prediction method. The results can be beneficial in real-life support of epilepsy patients.

scholarly journals Landslide Susceptibility Assessment Based on Different MaChine Learning Methods in Zhaoping County of Eastern Guangxi

2021 ◽  
Vol 13 (18) ◽  
pp. 3573
Author(s):  
Chunfang Kong ◽  
Yiping Tian ◽  
Xiaogang Ma ◽  
Zhengping Weng ◽  
Zhiting Zhang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Random Forest ◽  
Landslide Susceptibility ◽  
Roc Curves ◽  
Support Vector ◽  
Swarm Optimization ◽  
Svm Model ◽  
Vector Machines ◽  
Susceptibility Evaluation ◽  
Landslide Disaster

Regarding the ever increasing and frequent occurrence of serious landslide disaster in eastern Guangxi, the current study was implemented to adopt support vector machines (SVM), particle swarm optimization support vector machines (PSO-SVM), random forest (RF), and particle swarm optimization random forest (PSO-RF) methods to assess landslide susceptibility in Zhaoping County. To this end, 10 landslide disaster-related variables including digital elevation model (DEM)-derived, meteorology-derived, Landsat8-derived, geology-derived, and human activities factors were provided. Of 345 landslide disaster locations found, 70% were used to train the models, and the rest of them were performed for model verification. The aforementioned four models were run, and landslide susceptibility evaluation maps were produced. Then, receiver operating characteristics (ROC) curves, statistical analysis, and field investigation were performed to test and verify the efficiency of these models. Analysis and comparison of the results denoted that all four landslide models performed well for the landslide susceptibility evaluation as indicated by the area under curve (AUC) values of ROC curves from 0.863 to 0.934. Among them, it has been shown that the PSO-RF model has the highest accuracy in comparison to other landslide models, followed by the PSO-SVM model, the RF model, and the SVM model. Moreover, the results also showed that the PSO algorithm has a good effect on SVM and RF models. Furthermore, the landslide models devolved in the present study are promising methods that could be transferred to other regions for landslide susceptibility evaluation. In addition, the evaluation results can provide suggestions for disaster reduction and prevention in Zhaoping County of eastern Guangxi.

TEXTURE SEGMENTATION USING SEMI-SUPERVISED SUPPORT VECTOR MACHINES

2004 ◽  
Vol 04 (02) ◽  
pp. 131-142 ◽  
Author(s):  
SAEID SANEI
Keyword(s):  
Support Vector Machines ◽  
High Spatial Frequency ◽  
Misclassification Error ◽  
Support Vector ◽  
Frequency Noise ◽  
Multiple Constraints ◽  
Training Set ◽  
Vector Machines ◽  
Segmentation Algorithms ◽  
A Minor

Segmentation of natural textures has been investigated by developing a novel semi-supervised support vector machines (S3VM) algorithm with multiple constraints. Unlike conventional segmentation algorithms the proposed method does not classify the textures but classifies the uniform-texture regions and the regions of boundaries. Also the overall algorithm does not use any training set as used by all other learning algorithms such as conventional SVMs. During the process, the images are restored from high spatial frequency noise. Then various-order statistics of the textures within a sliding two-dimensional window are measured. K-mean algorithm is used to initialise the clustering procedure by labelling part of the class members and the classifier parameters. Therefore at this stage we have both the training and the working sets. A non-linear S3VM is then developed to exploit both sets to classify all the regions. The convex algorithm maximises a defined cost function by incorporating a number of constraints. The algorithm has been applied to combinations of a number of natural textures. It is demonstrated that the algorithm is robust, with negligible misclassification error. However, for complex textures there may be a minor misplacement of the edges.

Sign in / Sign up

Export Citation Format

Share Document