scholarly journals Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1166
Author(s):  
Bashir Musa ◽  
Nasser Yimen ◽  
Sani Isah Abba ◽  
Humphrey Hugh Adun ◽  
Mustafa Dagbasi
Keyword(s):  
Support Vector Regression ◽  
Mean Square Error ◽  
Prediction Accuracy ◽  
Demand Forecasting ◽  
Load Forecasting ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Hybrid Renewable Energy System

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

scholarly journals Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam

2020 ◽  
Vol 12 (11) ◽  
pp. 1814
Author(s):  
Phamchimai Phan ◽  
Nengcheng Chen ◽  
Lei Xu ◽  
Zeqiang Chen
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Vegetation Index ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Multi Temporal

Tea is a cash crop that improves the quality of life for people in the Tanuyen District of Laichau Province, Vietnam. Tea yield, however, has stagnated in recent years, due to changes in temperature, precipitation, the age of the tea bushes, and diseases. Developing an approach for monitoring tea bushes by remote sensing and Geographic Information Systems (GIS) might be a way to alleviate this problem. Using multi-temporal remote sensing data, the paper details an investigation of the changes in tea health and yield forecasting through the normalized difference vegetation index (NDVI). In this study, we used NDVI as a support tool to demonstrate the temporal and spatial changes in NDVI through the extract tea NDVI value and calculate the mean NDVI value. The results of the study showed that the minimum NDVI value was 0.42 during January 2013 and February 2015 and 2016. The maximum NDVI value was in August 2015 and June 2017. We indicate that the linear relationship between NDVI value and mean temperature was strong with R 2 = 0.79 Our results confirm that the combination of meteorological data and NDVI data can achieve a high performance of yield prediction. Three models to predict tea yield were conducted: support vector machine (SVM), random forest (RF), and the traditional linear regression model (TLRM). For period 2009 to 2018, the prediction tea yield by the RF model was the best with a R 2 = 0.73 , by SVM it was 0.66, and 0.57 with the TLRM. Three evaluation indicators were used to consider accuracy: the coefficient of determination ( R 2 ), root-mean-square error (RMSE), and percentage error of tea yield (PETY). The highest accuracy for the three models was in 2015 with a R 2 ≥ 0.87, RMSE < 50 kg/ha, and PETY less 3% error. In the other years, the prediction accuracy was higher in the SVM and RF models. Meanwhile, the RF algorithm was better than PETY (≤10%) and the root mean square error for this algorithm was significantly less (≤80 kg/ha). RMSE and PETY showed relatively good values in the TLRM model with a RMSE from 80 to 100 kg/ha and a PETY from 8 to 15%.

scholarly journals Comparing Four Types Methods for Karst NDVI Prediction Based on Machine Learning

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1341
Author(s):  
Yuju Ma ◽  
Liyuan Zuo ◽  
Jiangbo Gao ◽  
Qiang Liu ◽  
Lulu Liu
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Human Activities ◽  
Vegetation Index ◽  
Terrestrial Ecosystem ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Response Factors

As a link for energy transfer between the land and atmosphere in the terrestrial ecosystem, karst vegetation plays an important role. Karst vegetation is not only affected by environmental factors but also by intense human activities. The nonlinear characteristics of vegetation growth are induced by the interaction mechanism of these factors. Previous studies of this relationship were not comprehensive, and it is necessary to further explore it using a suitable method. In this study, we selected climate, human activities, topography, and soil texture as the response factors; a nonlinear relationship model between the karst normalized difference vegetation index (NDVI) and these factors was established by applying a back propagation neural network (BPNN), a radial basis function neural network (RBFNN), the random forest (RF) algorithm, and support vector regression (SVR); and then, the karst NDVI was predicted. The coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the obtained results were calculated, and the mean R2 values of the BPNN, RBFNN, RF, and SVR models were determined to be 0.77, 0.86, 0.89, and 0.91, respectively. Compared with the BPNN, RBFNN, and RF models, the SVR model had the lowest errors, with mean MSE, RMSE, and MAPE values of 0.001, 0.02, and 2.77, respectively. The results show that the BPNN, RBFNN, RF, and SVR models are within acceptable ranges for karst NDVI prediction, but the overall performance of the SVR model is the best, and it is more suitable for karst vegetation prediction.

scholarly journals Multiple-Forecasting Approach: A Prediction of CO2 Emission from the Paddy Crop in India

2021 ◽  
Author(s):  
Pawan Kumar Singh ◽  
Alok Kumar Pandey ◽  
Sahil Ahuja ◽  
Ravi Kiran
Keyword(s):  
Support Vector Regression ◽  
Mean Square Error ◽  
Co2 Emission ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Absolute Percentage Error ◽  
The Mean ◽  
Paddy Crop

Abstract This paper compares four prediction methods namely Random Forest Regressor (RFR), SARIMAX, Holt-Winters (H-W), and the Support Vector Regression (SVR) to forecast the total CO2 emission from the paddy crop in India. The major objective of this study is to compare these four models to suggest an effective model to predict the total CO2 emission. Data from 1961 to 2018 has been categorised into two parts: training and test data. The study forecasts total CO2 emission from paddy crop in India from 2019 to 2025. A comparison of mean absolute percentage error (MAPE) and the mean square error (MSE), highlights the differences in accuracy among the four models. The mean absolute percentage error (MAPE) and the mean square error (MSE) for the four methods are: RFR (MAPE: 5.67; MSE: 549900.02), SARIMAX (MAPE:1.67; MSE:70422.35), H-W (MAPE:0.75; MSE:16648.58), and SVR (MAPE: 0.91; MSE: 17832.4). The values of MAPE and MSE with the Holt-Winters (H-W) and the Support Vector Regression (SVR) is relatively low as compared to SARIMAX and RFR. On the basis of these results, it can be inferred that H-W and SVR were found suitable models to forecast the total CO2 emission from paddy crop. Holt-Winters the model predicted 14364.97 for the year 2025 and SVR predicted 13696.67 for the year 2025. These predictions can be used by the decision-maker to build a suitable policy for future studies. For further research, this approach can be contrasted with other approaches, such as the Neural Network or other forecasting methods, using more important datasets to train the model to achieve better forecast accuracy.

scholarly journals Electrical Load Forecasting using SVM Algorithm

2020 ◽  
Vol 8 (6) ◽  
pp. 4811-4816
Keyword(s):  
Support Vector Machine ◽  
Load Forecasting ◽  
Percentage Error ◽  
Support Vector ◽  
Live Load ◽  
Electric Load ◽  
Mean Square ◽  
Electrical Load ◽  
Load Demand ◽  
Svm Algorithm

Electrical load demand is variable in nature. Also, with the increase in technological development and automation, electric load demand tends to rise with time. For this, our generation facilities should be adequate 24x7 to meet the consumer’s load demand effectively. Therefore, load demand needs to be predicted or forecasted to avoid the energy crisis. In this paper, support vector machine (SVM) algorithm is explored for electric load forecasting. The live load data for the period of three months i.e., January to March, 2015, from a typical 66kV sub-station of the Punjab State Power Corporation Limited (PSPCL) for a selected site at Bhai Roopa sub-station, Bathinda, situated in the Punjab state of India, is acquired for the presented simulation study. The collected live data is divided into three categories, i.e., validation, training, and testing for the simulation study considering a SVM approach. Then, based on the environmental data input for the next 50 hours, the electric load is predicted. The obtained results from simulation were validated with the live load data of the selected site and found to be within the permissible limits. The mean square error (MSE), root-mean-square error (RMSE), mean absolute error (MAE), absolute percentage error (APE), mean absolute percentage error (MAPE) and sum of squares error (SSE) were calculated to show the effectiveness of the proposed support vector machine (SVM) algorithm based STLF. SVM is one of the effective machine learning algorithms. The errors so obtained clearly suggest that the proposed SVM algorithm gives reasonably accurate results, and is reliable for electric load forecasting.

scholarly journals Forecasting Electricity Consumption Using Time Series Model

2018 ◽  
Vol 7 (4.30) ◽  
pp. 218 ◽  
Author(s):  
Y.W. Lee ◽  
K.G. Tay ◽  
Y.Y. Choy
Keyword(s):  
Mean Square Error ◽  
Future Development ◽  
Linear Trend ◽  
Moving Average ◽  
Demand Forecasting ◽  
Electricity Consumption ◽  
Electricity Sector ◽  
Percentage Error ◽  
Mean Square ◽  
Monthly Electricity Consumption

Electricity demand forecasting is important for planning and facility expansion in the electricity sector.  Accurate forecasts can save operating and maintenance costs, increased the reliability of power supply and delivery system, and correct decisions for future development.  Universiti Tun Hussein Onn Malaysia (UTHM) which is a developing university in Malaysia has been growing since its formation in 1993.  Thus, it is important for UTHM to forecast the electricity consumption in future so that the future development can be determined.  Hence, UTHM electricity consumption was forecasted by using the simple moving average (SMA), weighted moving average (WMA), simple exponential smoothing (SES), Holt linear trend (HL), Holt-Winters (HW) and centered moving average (CMA).  The monthly electricity consumption from January 2011 to December 2017 was used to forecast January to December 2018 monthly electricity consumption.  HW gives the smallest mean absolute error (MAE) and mean absolute percentage error (MAPE), while CMA produces the lowest mean square error (MSE) and root mean square error (RMSE).  As there is a decreasing population of UTHM after the moving of four faculties to Pagoh and HW forecasted trend is decreasing whereas CMA is increasing, hence HW might forecast better in this problem.

Development of Flood Forecasting System for Someshwari-Kangsa Sub-watershed of Bangladesh-India Using Different Machine Learning Techniques

2021 ◽  
Author(s):  
Md Hamidul Haque ◽  
Mushtari Sadia ◽  
Mashiat Mustaq
Keyword(s):  
Machine Learning ◽  
Water Level ◽  
Mean Square Error ◽  
Lead Time ◽  
Flood Forecasting ◽  
Data Driven ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Mean Square ◽  
Physically Based

&lt;p&gt;Floods are natural disasters caused mainly due to heavy or excessive rainfall. They induce massive economic losses in Bangladesh every year. Physically-based flood prediction models have been used over the years where simplified forms of physical laws are used to reduce calculations' complexity. It sometimes leads to oversimplification and inaccuracy in the prediction. Moreover, a physically-based model requires intensive monitoring datasets for calibration, accurate soil properties information, and a heavy computational facility, creating an impediment for quick, economical and precise short-term prediction. Researchers have tried different approaches like empirical data-driven models, especially machine learning-based models, to offer an alternative approach to the physically-based models but focused on developing only one machine learning (ML) technique at a time (i.e., ANN, MLP, etc.). There are many other techniques, algorithms, and models in machine learning (ML) technology that have the potential to be effective and efficient in flood forecasting. In this study, five different machine learning algorithms- exponent back propagation neural network (EBPNN), multilayer perceptron (MLP), support vector regression (SVR), DT Regression (DTR), and extreme gradient boosting (XGBoost) were used to develop total 180 independent models based on a different combination of time lags for input data and lead time in forecast. Models were developed for Someshwari-Kangsa sub-watershed of Bangladesh's North Central hydrological region with 5772 km&lt;sup&gt;2&lt;/sup&gt; drainage area. It is also a data-scarce region with only three hydrological and hydro-meteorological stations for the whole sub-watershed. This region mostly suffers extreme meteorological events driven flooding. Therefore, satellite-based precipitation, temperature, relative humidity, wind speed data, and observed water level data from the Bangladesh Water Development Board (BWDB) were used as input and response variables.&lt;/p&gt;&lt;p&gt;For comparison, the accuracy of these models was evaluated using different statistical indices - coefficient of determination, mean square error (MSE), mean absolute error (MAE), mean relative error (MRE), explained variance score and normalized centred root mean square error (NCRMSE). Developed models were ranked based on the coefficient of determination (R&lt;sup&gt;2&lt;/sup&gt;) value. All the models performed well with R&lt;sup&gt;2&lt;/sup&gt; being greater than 0.85 in most cases. Further analysis of the model results showed that most of the models performed well for forecasting 24-hour lead time water level. Models developed using XGBoost algorithm outperformed other models in all metrics. Moreover, each of the algorithms' best-performed models was extended further up to 20 days lead time to generate forecasting horizon. Models demonstrated remarkable consistency in their performance with the coefficient of determination (R&lt;sup&gt;2&lt;/sup&gt;) being greater than 0.70 at 20 days lead-time of forecasting horizon in most cases except the DTR-based model. For 10- and 5-days lead time of forecasting horizon, it was greater than 0.75 and 0.80 respectively, for all the model extended. This study concludes that the machine algorithm-based data-driven model can be a powerful tool for flood forecasting in data-scarce regions with excellent accuracy, quick building and running time, and economic feasibility.&lt;/p&gt;

A Quantitative Approach to Measure Webpage Aesthetics

2020 ◽  
Vol 16 (2) ◽  
pp. 53-68
Author(s):  
Ranjan Maity ◽  
Samit Bhattacharya
Keyword(s):  
Root Mean Square Error ◽  
Prediction Model ◽  
Computational Model ◽  
Support Vector Regression ◽  
Mean Square Error ◽  
Cross Validation ◽  
Support Vector ◽  
Mean Square ◽  
Validation Technique ◽  
Fold Cross Validation

Aesthetics measurement is important in determining and improving the usability of a webpage. Wireframe models, the collection of the rectangular objects, can approximate the size and positions of the different webpage elements. The positional geometry of these objects is primarily responsible for determining aesthetics as shown in studies. In this work, the authors propose a computational model for predicting webpage aesthetics based on the positional geometry features. In this study, the authors found that ten out of the thirteen reported features are statistically significant for webpage aesthetics. Using these ten features, the authors developed a computational model for webpage aesthetics prediction. The model works on the basis of support vector regression. The authors rated the wireframe models of 209 webpages by 150 participants. The average users' ratings and the ten significant features' values were used to train and test the aesthetics prediction model. Five-fold cross-validation technique shows the model can predict aesthetics with a Root Mean Square Error (RMSE) of only 0.42.

scholarly journals Machine learning assisted Cameriere method for dental age estimation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shihui Shen ◽  
Zihao Liu ◽  
Jian Wang ◽  
Linfeng Fan ◽  
Fang Ji ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mean Square Error ◽  
Age Estimation ◽  
Estimation Method ◽  
Permanent Teeth ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Mean Square ◽  
Dental Age ◽  
Dental Age Estimation

Abstract Background Recently, the dental age estimation method developed by Cameriere has been widely recognized and accepted. Although machine learning (ML) methods can improve the accuracy of dental age estimation, no machine learning research exists on the use of the Cameriere dental age estimation method, making this research innovative and meaningful. Aim The purpose of this research is to use 7 lower left permanent teeth and three models [random forest (RF), support vector machine (SVM), and linear regression (LR)] based on the Cameriere method to predict children's dental age, and compare with the Cameriere age estimation. Subjects and methods This was a retrospective study that collected and analyzed orthopantomograms of 748 children (356 females and 392 males) aged 5–13 years. Data were randomly divided into training and test datasets in an 80–20% proportion for the ML algorithms. The procedure, starting with randomly creating new training and test datasets, was repeated 20 times. 7 permanent developing teeth on the left mandible (except wisdom teeth) were recorded using the Cameriere method. Then, the traditional Cameriere formula and three models (RF, SVM, and LR) were used to estimate the dental age. The age prediction accuracy was measured by five indicators: the coefficient of determination (R2), mean error (ME), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). Results The research showed that the ML models have better accuracy than the traditional Cameriere formula. The ME, MAE, MSE, and RMSE values of the SVM model (0.004, 0.489, 0.392, and 0.625, respectively) and the RF model (− 0.004, 0.495, 0.389, and 0.623, respectively) were lower with the highest accuracy. In contrast, the ME, MAE, MSE and RMSE of the European Cameriere formula were 0.592, 0.846, 0.755, and 0.869, respectively, and those of the Chinese Cameriere formula were 0.748, 0.812, 0.890 and 0.943, respectively. Conclusions Compared to the Cameriere formula, ML methods based on the Cameriere’s maturation stages were more accurate in estimating dental age. These results support the use of ML algorithms instead of the traditional Cameriere formula.

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