scholarly journals Support Vector Machine to Predict Electricity Consumption in the Energy Management Laboratory

2021 ◽  
Vol 5 (3) ◽  
pp. 466-473
Author(s):  
Azam Zamhuri Fuadi ◽  
Irsyad Nashirul Haq ◽  
Edi Leksono
Keyword(s):  
Support Vector Machine ◽  
Energy Management ◽  
Mean Squared Error ◽  
Measurement Data ◽  
Electricity Consumption ◽  
Absolute Error ◽  
Support Vector ◽  
Mean Error ◽  
Squared Error ◽  
Electrical Loads

Predicted electricity consumption is needed to perform energy management. Electricity consumption prediction is also very important in the development of intelligent power grids and advanced electrification network information. we implement a Support Vector Machine (SVM) to predict electrical loads and results compared to measurable electrical loads. Laboratory electrical loads have their own characteristics when compared to residential, commercial, or industrial, we use electrical load data in energy management laboratories to be used to be predicted. C and Gamma as searchable parameters use GridSearchCV to get optimal SVM input parameters. Our prediction data is compared to measurement data and is searched for accuracy based on RMSE (Root Square Mean Error), MAE (Mean Absolute Error) and MSE (Mean Squared Error) values. Based on this we get the optimal parameter values C 1e6 and Gamma 2.97e-07, with the result RSME (Root Square Mean Error) ; 0.37, MAE (meaning absolute error); 0.21 and MSE (Mean Squared Error); 0.14.

scholarly journals Forecasting of Air Pollution Index PM2.5 Using Support Vector Machine(SVM)

2020 ◽  
Vol 5 (3) ◽  
pp. 43-53
Author(s):  
Nor Hayati Binti Shafii ◽  
Rohana Alias ◽  
Nur Fithrinnissaa Zamani ◽  
Nur Fatihah Fauzi
Keyword(s):  
Air Pollution ◽  
Support Vector Machine ◽  
Air Quality ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Pollution Index ◽  
Absolute Error ◽  
Support Vector ◽  
Air Pollution Index ◽  
Squared Error

Air pollution is a current monitored problem in areas with high population density such as big cities. Many regions in Malaysia are facing extreme air quality issues. This situation is caused by several factors such as human behavior, environmental awareness and technological development.  Accessing the air pollution index (API) accurately is very important to control its impact on environmental and human health.  The work presented here aims to access air pollution index of PM2.5 using Support Vector Machine (SVM) and to compare the accuracy of four different types of the kernel function in Support Vector Machine (SVM).  The data used is provided by the Department of Environment (DOE) and it is recorded from two Continuous Air Quality Monitoring Stations (CAQM) located at Tanah Merah and Kota Bharu. The results are analyzed using mean absolute error (MAE) and root mean squared error (RMSE). It is found that the proposed model using Radial Basis Function (RBF) with its parameters of cost and gamma equal to 100 can effectively and accurately forecast the air pollution index with Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 0.03868583 and 0.06251793 respectively for API in Kota Bharu and 0.03857308 (MAE) and 0.05895648 (RMSE) for API in Tanah Merah.

Electricity Consumption Analysis Using Demographic Variables Case Study – Nakhonratchasima, Thailand

2013 ◽  
Vol 734-737 ◽  
pp. 1679-1682
Author(s):  
Sureeporn Meehom ◽  
Nopphadon Khodpun
Keyword(s):  
High Speed ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Electricity Consumption ◽  
Absolute Error ◽  
Demographic Variables ◽  
Resources Management ◽  
Squared Error ◽  
High Speed Diesel

Electricity energy is vital in social and economic for nation development. The electricity consumption analysis plays an important role for sustainable energy and electricity resources management in the future. In this paper, the influence of demographical variables on the annual electricity consumption in Nakhonratchasima has been investigated by multiple regression analysis. It is founded that the electricity consumption correlated with four demographic variables, which are the number of electricity consumers, the amount of high speed diesel usages, the number of industrial factory and the number of employed labor force. The historical electricity consumption and all variables for the period 20022010 have been analyzed in 8 models for electricity prediction in 2011. In conclusion, the effective model has been selected by comparison of adjusted R2, mean absolute error (MAE) and root mean squared error (RMSE) of the proposed models. Model 8 is acceptable in relation to electricity consumption analysis with adjusted-R2, RMSE and MAE equal to 0.9980, 0.7540% and 0.6095% respectively. The results indicate that the model using all four variables has strong ability to predict future annual electricity consumption with 4,195,837,877 kWh in 2011.

scholarly journals Visualization & Prediction of COVID-19 Future Outbreak by Using Machine Learning

2021 ◽  
Vol 13 (3) ◽  
pp. 16-32
Author(s):  
Ahmed Hassan Mohammed Hassan ◽  
◽  
Arfan Ali Mohammed Qasem ◽  
Walaa Faisal Mohammed Abdalla ◽  
Omer H. Elhassan
Keyword(s):  
Machine Learning ◽  
Polynomial Regression ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Future Perspective ◽  
Support Vector ◽  
Squared Error ◽  
Vector Machines ◽  
The World ◽  
Negative Factors

Day by day, the accumulative incidence of COVID-19 is rapidly increasing. After the spread of the Corona epidemic and the death of more than a million people around the world countries, scientists and researchers have tended to conduct research and take advantage of modern technologies to learn machine to help the world to get rid of the Coronavirus (COVID-19) epidemic. To track and predict the disease Machine Learning (ML) can be deployed very effectively. ML techniques have been anticipated in areas that need to identify dangerous negative factors and define their priorities. The significance of a proposed system is to find the predict the number of people infected with COVID19 using ML. Four standard models anticipate COVID-19 prediction, which are Neural Network (NN), Support Vector Machines (SVM), Bayesian Network (BN) and Polynomial Regression (PR). The data utilized to test these models content of number of deaths, newly infected cases, and recoveries in the next 20 days. Five measures parameters were used to evaluate the performance of each model, namely root mean squared error (RMSE), mean squared error (MAE), mean absolute error (MSE), Explained Variance score and r2 score (R2). The significance and value of proposed system auspicious mechanism to anticipate these models for the current cenario of the COVID-19 epidemic. The results showed NN outperformed the other models, while in the available dataset the SVM performs poorly in all the prediction. Reference to our results showed that injuries will increase slightly in the coming days. Also, we find that the results give rise to hope due to the low death rate. For future perspective, case explanation and data amalgamation must be kept up persistently.

Visible-near infrared spectroscopy for detection of blood in sheep faeces

2020 ◽  
Vol 28 (5-6) ◽  
pp. 255-266 ◽  
Author(s):  
Elise A Kho ◽  
Jill N Fernandes ◽  
Andrew C Kotze ◽  
Glen P Fox ◽  
Maggy Lord ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Root Mean Squared Error ◽  
Squared Error

Heavy infestations of the blood-sucking gastrointestinal nematodes, Haemonchus contortus can cause severe anaemia in sheep and leakage of blood into the faeces, leading to morbidity and mortality. Early and accurate diagnosis of infections is critical for timely treatment of sheep, minimizing production and sheep welfare impacts. In pursuit of a quick and easy measure of H. contortus infections, we investigated the use of portable visible near infrared spectrometers for detecting the presence of haemoglobin in sheep faeces as an indicator of H. contortus infection. Calibration models built within the 400–600 nm region by partial least square regression resulted in acceptable prediction accuracies (r 2 p > 0.70 and root mean squared error of prediction <2.64 µg Hb mg−1 faeces) for haemoglobin quantification using two spectrometers. The prediction results from support vector machine regression further improved the prediction of haemoglobin in moist sheep faeces (r 2 p > 0.87 and root mean squared error of prediction <2.00 µg haemoglobin mg−1 faeces). Based on a threshold for anthelmintic treatment of 3 µg Hb mg−1 faeces, both the partial least square and support vector machine models showed high sensitivity (89%) and high specificity (>77%). The specificity of the prediction model for detecting haemoglobin in sheep faeces may be improved by adding more variations in faecal composition into the calibration model. Our success in detecting haemoglobin in sheep faeces, following minimal sample preparation, suggests that with further development, vis–near infrared spectroscopy can provide a sensitive and convenient method for on-farm diagnosis of H. contortus infections.

scholarly journals Methane Detection Based on Improved Chicken Algorithm Optimization Support Vector Machine

2019 ◽  
Vol 9 (9) ◽  
pp. 1761 ◽  
Author(s):  
Zhifang Wang ◽  
Shutao Wang ◽  
Deming Kong ◽  
Shiyu Liu
Keyword(s):  
Support Vector Machine ◽  
Optimization Algorithm ◽  
Methane Concentration ◽  
Mean Squared Error ◽  
Support Vector ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Squared Error ◽  
Svm Model ◽  
Methane Detection

Methane, known as a flammable and explosion hazard gas, is the main component of marsh gas, firedamp, and rock gas. Therefore, it is important to be able to detect methane concentration safely and effectively. At present, many models have been proposed to enhance the performance of methane predictions. However, the traditional models displayed inevitable shortcomings in parameter optimization in our experiment, which resulted in their having poor prediction performance. Accordingly, the improved chicken swarm algorithm optimized support vector machine (ICSO-SVM) was proposed to predict the concentration of methane precisely. The traditional chicken swarm optimization algorithm (CSO) easily falls into a local optimum due to its characteristics, so the ICSO algorithm was developed. The formula for position updating of the chicks of the ICSO is not only about the rooster of the same subgroup, but also about the roosters of other subgroups. Therefore, the ICSO algorithm more easily avoids falling into the local extremum. In this paper, the following work has been done. The sample data were obtained by using the methane detection system designed by us; In order to verify the validity of the ICSO algorithm, the ICSO, CSO, genetic algorithm (GA), and particle swarm optimization algorithm (PSO) algorithms were tested, and the four models were applied for methane concentration prediction. The results showed that he ICSO algorithm had the best convergence effect, relative error percentage, and average mean squared error, when the four models were applied to predict methane concentration. The results showed that the average mean squared error values of ICSO-SVM model were smaller than other three models, and that the ICSO-SVM model has better stability, and the average recovery rate of the ICSO-SVM is much closer to 100%. Therefore, the ICSO-SVM model can efficiently predict methane concentration.

Modelling and Forecasting Portfolio Inflows

2022 ◽  
pp. 1427-1448
Author(s):  
Mogari I. Rapoo ◽  
Elias Munapo ◽  
Martin M. Chanza ◽  
Olusegun Sunday Ewemooje
Keyword(s):  
Banking Sector ◽  
Mean Squared Error ◽  
Model Performance ◽  
Absolute Error ◽  
Support Vector ◽  
Vector Autoregressive ◽  
Squared Error ◽  
Daily Data ◽  
Determining Factors ◽  
Push Factor

This chapter analyses efficiency of support vector regression (SVR), artificial neural networks (ANNs), and structural vector autoregressive (SVAR) models in terms of in-sample forecasting of portfolio inflows (PIs). Time series daily data sourced from Rand Merchant Bank (RMB) covering the period of 1st March 2004 to 1st February 2016 were used. Mean squared error, root mean squared error, mean absolute error, mean absolute squared error, and root mean scaled log error were used to evaluate model performance. The results showed that SVR has the best modelling performance when compared to others. In determining factors that affect allocation of PIs into South Africa based on SVAR, 69% of the variation was explained by pull factors while 9% was explained by push factor. Hence, SVR model is more accurate than ANNs. This chapter therefore recommends that banking sector particularly RMB should use machine learning technique in modelling PIs for a better financial solution.

scholarly journals Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt

2019 ◽  
Vol 9 (15) ◽  
pp. 3172 ◽  
Author(s):  
Hoang-Long Nguyen ◽  
Thanh-Hai Le ◽  
Cao-Thang Pham ◽  
Tien-Thinh Le ◽  
Lanh Si Ho ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machine ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Support Vector ◽  
Inference System ◽  
Stone Matrix Asphalt ◽  
Hybrid Artificial Intelligence ◽  
Stone Matrix

The main objective of this study is to develop and compare hybrid Artificial Intelligence (AI) approaches, namely Adaptive Network-based Fuzzy Inference System (ANFIS) optimized by Genetic Algorithm (GAANFIS) and Particle Swarm Optimization (PSOANFIS) and Support Vector Machine (SVM) for predicting the Marshall Stability (MS) of Stone Matrix Asphalt (SMA) materials. Other important properties of the SMA, namely Marshall Flow (MF) and Marshall Quotient (MQ) were also predicted using the best model found. With that goal, the SMA samples were fabricated in a local laboratory and used to generate datasets for the modeling. The considered input parameters were coarse and fine aggregates, bitumen content and cellulose. The predicted targets were Marshall Parameters such as MS, MF and MQ. Models performance assessment was evaluated thanks to criteria such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and correlation coefficient (R). A Monte Carlo approach with 1000 simulations was used to deduce the statistical results to assess the performance of the three proposed AI models. The results showed that the SVM is the best predictor regarding the converged statistical criteria and probability density functions of RMSE, MAE and R. The results of this study represent a contribution towards the selection of a suitable AI approach to quickly and accurately determine the Marshall Parameters of SMA mixtures.

scholarly journals Prediction of soil loss due to erosion using support vector machine model

2020 ◽  
Vol 42 (3) ◽  
Author(s):  
Van Quan Tran ◽  
Indra Prakash
Keyword(s):  
Support Vector Machine ◽  
Soil Erosion ◽  
Vegetation Cover ◽  
Soil Loss ◽  
Rainfall Intensity ◽  
Mean Squared Error ◽  
Support Vector ◽  
Squared Error ◽  
Model Study ◽  
Loss Prediction

Soil erosion refers to the detachment and removal of soil particles from land (topsoil), by the natural physical forces (water, glacier and wind). Soil erosion causes soil loss in the catchment or any land areas severely impacting agriculture activity, sedimentation in the dam reservoirs, and hampering developmental activities. Therefore, it is desirable to accurately measure soil loss due to erosion for the development and management of an area. With this objective, a well-known machine learning algorithm Support Vector Machine (SVM) has been used in the development of the soil loss prediction model. Eight erosion affecting variable inputs: ambient temperature Tair, rainfall, Antecedent Moisture Conditions (AMC), rainfall intensity, slope, vegetation cover, soil temperature Tsoil and moisture of the soil. Data on published literature was used in the model study. The accuracy of the proposed SVM was assessed by using three statistical performance evaluation indicators namely Person correlation coefficient (R), Root Mean Squared Error (RMSE), Mean Squared Error (MAE). Partial Dependence Plots (PDP) was used to investigate the dependence of prediction results of eight input variables used in the model study. Model validation results showed that SVM model performed well for the prediction of soil loss for testing (R = 0.8993) and also for training (R=0.9123). Rainfall intensity and vegetation cover were found to be the two most important affecting input parameters for the soil loss prediction.

scholarly journals Predicting and Analysing the Behaviour of COVID-19

2021 ◽  
pp. 40-46
Author(s):  
Gaurav Singh ◽  
Shivam Rai ◽  
Himanshu Mishra ◽  
Manoj Kumar
Keyword(s):  
Machine Learning ◽  
Polynomial Regression ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Systems Science ◽  
Data Repository ◽  
Support Vector ◽  
Squared Error

The prime objective of this work is to predicting and analysing the Covid-19 pandemic around the world using Machine Learning algorithms like Polynomial Regression, Support Vector Machine and Ridge Regression. And furthermore, assess and compare the performance of the varied regression algorithms as far as parameters like R squared, Mean Absolute Error, Mean Squared Error and Root Mean Squared Error. In this work, we have used the dataset available on Covid-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at John Hopkins University. We have analyzed the covid19 cases from 22/1/2020 till now. We applied a supervised machine learning prediction model to forecast the possible confirmed cases for the next ten days.

scholarly journals Soft Computing Techniques for Weather Change Predictions in Delhi

2019 ◽  
Vol 8 (4) ◽  
pp. 793-800
Keyword(s):  
Soft Computing ◽  
Mean Squared Error ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Research Work ◽  
Absolute Error ◽  
Support Vector ◽  
Squared Error ◽  
Proposed Model ◽  
Soft Computing Techniques

Weather forecasting and warning is the application of science and technology to predict the state of the weather for a future time of a given location. The emergence of adverse effects of weather has endangered the life of general public in previous years. The unpredicted flood and super cyclone in many places have created havoc. The government and private agencies are working on its behaviours but still it is challenging and incomplete. But, the application of soft computing techniques in weather prediction has made a significant perfomance now a days. This research work presents the comparative study of soft computing techniques like MultiLayer Perceptron(MLP), Support Vector Machine(SVM) and J48 Decision Tree for forecasting the weather of Delhi with ten years data comprising of temperature, dew, humidity, air pressure, wind speed and visibility. This paper tries to describe the comparison among above models using four different error values like Relative Absolute Error(RAE), Mean Absolute Error(MAE), Root Mean Squared Error(RMSE) and Root Relative Squared Error(R2 ) with a proposed model by defining new algorithm. Further the performance can be enhanced if textmining will be applied in this proposed model.

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