scholarly journals Equation Based New Methods for Residential Load Forecasting

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6378
Author(s):  
S. M. Mahfuz Alam ◽  
Mohd. Hasan Ali
Keyword(s):  
Root Mean Square ◽  
Load Forecasting ◽  
Residential Building ◽  
The Other ◽  
Support Vector ◽  
Linear Regression Method ◽  
Mean Square ◽  
Performance Indices ◽  
Degree Days ◽  
Average Percentage

This work proposes two non-linear and one linear equation-based system for residential load forecasting considering heating degree days, cooling degree days, occupancy, and day type, which are applicable to any residential building with small sets of smart meter data. The coefficients of the proposed nonlinear and linear equations are tuned by particle swarm optimization (PSO) and the multiple linear regression method, respectively. For the purpose of comparison, a subtractive clustering based adaptive neuro fuzzy inference system (ANFIS), random forests, gradient boosting trees, and long-term short memory neural network, conventional and modified support vector regression methods were considered. Simulations have been performed in MATLAB environment, and all the methods were tested with randomly chosen 30 days data of a residential building in Memphis City for energy consumption prediction. The absolute average error, root mean square error, and mean average percentage errors are tabulated and considered as performance indices. The efficacy of the proposed systems for residential load forecasting over the other systems have been validated by both simulation results and performance indices, which indicate that the proposed equation-based systems have the lowest absolute average errors, root mean square errors, and mean average percentage errors compared to the other methods. In addition, the proposed systems can be easily practically implemented.

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%.

Determination of Magnesium Oxide Content in Mineral Medicine Talcum Using Near-Infrared Spectroscopy Integrated with Support Vector Machine

2017 ◽  
Vol 71 (11) ◽  
pp. 2427-2436 ◽  
Author(s):  
Mi Lei ◽  
Long Chen ◽  
Bisheng Huang ◽  
Keli Chen
Keyword(s):  
Root Mean Square Error ◽  
Least Squares ◽  
Partial Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Support Vector ◽  
Mean Square ◽  
Characteristic Spectrum

In this research paper, a fast, quantitative, analytical model for magnesium oxide (MgO) content in medicinal mineral talcum was explored based on near-infrared (NIR) spectroscopy. MgO content in each sample was determined by ethylenediaminetetraacetic acid (EDTA) titration and taken as reference value of NIR spectroscopy, and then a variety of processing methods of spectra data were compared to establish a good NIR spectroscopy model. To start, 50 batches of talcum samples were categorized into training set and test set using the Kennard–Stone (K-S) algorithm. In a partial least squares regression (PLSR) model, both leave-one-out cross-validation (LOOCV) and training set validation (TSV) were used to screen spectrum preprocessing methods from multiplicative scatter correction (MSC), and finally the standard normal variate transformation (SNV) was chosen as the optimal pretreatment method. The modeling spectrum bands and ranks were optimized using PLSR method, and the characteristic spectrum ranges were determined as 11995–10664, 7991–6661, and 4326–3999 cm−1, with four optimal ranks. In the support vector machine (SVM) model, the radical basis function (RBF) kernel function was used. Moreover, the full spectrum data of samples pretreated with SNV, the characteristic spectrum data screened using synergy interval partial least squares (SiPLS), and the scoring data of the first four ranks obtained by a partial least squares (PLS) dimension reduction of characteristic spectrum were taken as input variables of SVM, and the MgO content reference values of various sample were taken as output values. In addition, the SVM model internal parameters were optimized using the grid optimization method (GRID), particle swarm optimization (PSO), and genetic algorithm (GA) so that the optimal C and g-values were determined and the validation model was established. By comprehensively comparing the validation effects of different models, it can be concluded that the scoring data of the first four ranks obtained by PLS dimension reduction of characteristic spectrum were taken as input variables of SVM, and the PLS-SVM regression model established using GRID was the optimal NIR spectroscopy quantitative model of talc. This PLS-SVM regression model (rank = 4) measured that the MgO content of talcum was in the range of 17.42–33.22%, with root mean square error of cross validation (RMSECV) of 2.2127%, root mean square error of calibration (RMSEC) of 0.6057%, and root mean square error of prediction (RMSEP) of 1.2901%. This model showed high accuracy and strong prediction capacity, which can be used for rapid prediction of MgO content in talcum.

Evaluation of High-Resolution Precipitation Products over Southwest China

2020 ◽  
Vol 21 (11) ◽  
pp. 2691-2712
Author(s):  
Yanbo Nie ◽  
Jianqi Sun
Keyword(s):  
High Resolution ◽  
Root Mean Square ◽  
Extreme Precipitation ◽  
Southwest China ◽  
Daily Precipitation ◽  
The Other ◽  
Future Research ◽  
Monthly Precipitation ◽  
Mean Square ◽  
Mean Square Errors

AbstractThe evaluation of gridded high-resolution precipitation products (HRPPs) is important in areas with complex topography, because rain gauges that are unevenly and sparsely distributed over an area cannot effectively reflect the spatial variabilities of the precipitation and related extremes in detail. In this study, the applicability of six satellite-based precipitation products (TMPA 3B42V7, IMERG, GSMaP-Gauge, CMORPH-CRT, PERSIANN-CDR, and GPCP) and five gauge-based precipitation products (APHRODITE, CN05.1, GPCC-D, GPCC-M, and CRU) over southwest China from 1998 to 2016 is evaluated by performing a comparison with meteorological station observations. The results show that GPCC-M exhibits the best performances for annual, seasonal, and monthly precipitation, which is supported by the lowest root-mean-square errors (RMSEs) for annual and seasonal precipitation and the lowest normalized root-mean-square error (NRMSE) for monthly precipitation. According to the NRMSE and critical success index (CSI), CN05.1 outperforms the other HRPPs at detecting daily precipitation; however, CN05.1 tends to overestimate the frequencies of light precipitation and underestimate the frequencies of heavy precipitation, which is reflected by the probability density function (PDF) for daily precipitation. The bias ratio (BIAS) and extreme precipitation indices show that IMERG shows numerous advantages over the other HRPPs in detecting extreme precipitation and estimating the precipitation intensity. Such results are helpful for future research on precipitation/extremes and related hydrometeorological disasters that occur throughout southwest China.

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 Multi-grained cascade forest for effluent quality prediction of papermaking wastewater treatment processes

2020 ◽  
Vol 81 (5) ◽  
pp. 1090-1098
Author(s):  
Chen Xin ◽  
Xueqing Shi ◽  
Dongsheng Wang ◽  
Chong Yang ◽  
Qian Li ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Correlation Coefficient ◽  
Root Mean Square ◽  
Environmental Conservation ◽  
Support Vector ◽  
Mean Square ◽  
A Value ◽  
Conventional Models ◽  
Papermaking Wastewater

Abstract The real time estimation of effluent indices of papermaking wastewater is vital to environmental conservation. Ensemble methods have significant advantages over conventional single models in terms of prediction accuracy. As an ensemble method, multi-grained cascade forest (gcForest) is implemented for the prediction of wastewater indices. Compared with the conventional modeling methods including partial least squares, support vector regression, and artificial neural networks, the gcForest model shows prediction superiority for effluent suspended solid (SSeff) and effluent chemical oxygen demand (CODeff). In terms of SSeff, gcForest achieves the highest correlation coefficient with a value of 0.86 and the lowest root-mean-square error (RMSE) value of 0.41. In comparison with the conventional models, the RMSE value using gcForest is reduced by approximately 46.05% to 50.60%. In terms of CODeff, gcForest achieves the highest correlation coefficient with a value of 0.83 and the lowest root-mean-square error value of 4.05. In comparison with the conventional models, the RMSE value using gcForest is reduced by approximately 10.60% to 18.51%.

scholarly journals Vibrodiagnostics Used for Evaluating the Technical Condition of a Mechanical Gearbox

2018 ◽  
Vol 198 ◽  
pp. 02002
Author(s):  
Jan Furch ◽  
Jiří Stodola ◽  
Josef Glos
Keyword(s):  
Life Cycle ◽  
Root Mean Square ◽  
Technical Condition ◽  
The Other ◽  
Technical Diagnostics ◽  
Mean Square ◽  
Crest Factor ◽  
Road Vehicle ◽  
Vibration Signals ◽  
Fourth Position

The aim of this article is to evaluate the technical condition of a mechanical four-speed gearbox tightly connected to a mechanical two-speed auxiliary gearbox placed in an off-road vehicle. When observing the technical condition of the mechanical gearbox, we used one of technical diagnostics methods, namely vibrodiagnostics. The mechanical gearbox was monitored during all its life cycle up to failure occurrence. In the article there is a detailed description of the stand where the gearbox technical condition was monitored. When observing vibration signals, we used four tri-axial sensors placed in four selected spots depending on the design arrangement of antifriction bearings. In order to evaluate the measured results, two methods were applied, namely the root mean square gRMS based on the measured acceleration in three axes. The other method used for monitoring the technical condition was the Crest factor. The aim of the experiment was to monitor the technical condition of the mechanical gearbox, mainly its antifriction bearings, therefore the values were measured when the gear was put into fourth position and into overdrive in the auxiliary gearbox. When the gear is put into fourth position, the torque is transmitted from the motor through the main gearbox and the auxiliary gearbox directly. Single gearbox wheels are engaged but not loaded (they do not transmit the torque). At the end of our article we introduced the measured values along with the oral evaluation of the technical condition of the mechanical gearbox and the auxiliary gearbox.

scholarly journals A combined prediction approach based on wavelet transform for crop water requirement

2020 ◽  
Vol 20 (3) ◽  
pp. 1016-1034
Author(s):  
Zhongda Tian
Keyword(s):  
Wavelet Transform ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Water Requirement ◽  
Crop Water Requirement ◽  
Support Vector ◽  
Mean Square ◽  
Crop Water ◽  
Prediction Approach

Abstract The accurate prediction of crop water requirement is of great significance for the development of regional agriculture. Based on the wavelet transform, a combined prediction approach for crop water requirement is proposed. Firstly, the Mallat wavelet transform algorithm is used to decompose and reconstruct the crop water requirement series. The approximate and detail components of the original series can be obtained. The characteristics of approximate components and detail components are analyzed by Hurst index. Then, according to the different characteristics of the components, the particle swarm optimization algorithm optimized support vector machine is used to predict the approximate component, and the autoregressive moving average model is used to predict the detail components. Three-fold cross-validation is used to improve the generalization ability of the forecasting model. Finally, combined with the prediction value of each prediction model, the final prediction value of crop water requirement is obtained. The crop water requirement data from 1983 to 2018 in Liaoning Province of China are collected as the research object. The simulation results indicate that the proposed combined prediction approach has high prediction accuracy for crop water requirement. The comparison of performance indicators shows that the root mean square error of the proposed prediction approach reduced by 45.40% to 57.16%, mean absolute error reduced by 32.96% to 52.07%, mean absolute percentile error reduced by 33.02% to 52.37%, relative root mean square error reduced by 45.26% to 57.38%, square sum error reduced by 70.18% to 80.42%, and the Theil inequality coefficient reduced by 59.02% to 80.77%. R square increased by 16.46% to 54.77%, and the index of agreement increased by 3.82% to 23.37%. The results of Pearson's test and the DM test show that the association strength between the actual value and the prediction value of the crop water requirement is stronger. Moreover, the proposed prediction approach in this paper has higher reliability under the same confidence level. The effectiveness of the proposed prediction approach for crop water requirement is verified. The proposed prediction approach has great significance for the rational use of water resources, planning and management, promoting social and economic sustainable development.

scholarly journals Perbandingan Algoritma Support Vector Machine Biasa dan Support Vector Machine berbasis Particle Swarm Optimization untuk Prediksi Gempa Bumi

2018 ◽  
Vol 4 (1) ◽  
Author(s):  
Agustian Noor
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Particle Swarm ◽  
Support Vector ◽  
Mean Square ◽  
Swarm Optimization

Gempa merupakan fenomena alam secara periodik yang terjadi di seluruh belahan bumi akibat adanya gaya pembangkit pasang surut yang utamanya berasal dari matahari dan bulan. Tujuan penelitian ini adalah untuk menganalisa hasil gempa bumi di Sumara Utara. Metode yang diusulkan adalahmembandingkan SVM dan SVM-PSO yang menggunakan data dari instansi terkait khususnya di daerah Sumatra Utara, Masing-masing algoritma akan implementasikan dengan menggunakan RapidMiner 5.1 Pengukuran kinerja dilakukan dengan menghitung rata-rata error yang terjadi melalui besaran Root Mean Square Error (RMSE). Semakin kecil nilai dari masing-masing parameter kinerja ini menyatakan semakin dekat nilai prediksi dengan nilai sebenarnya. Dengan demikian dapat diketahui algoritma yang lebih akurat.

Accuracy Improvement of Solar Power Estimation Using Real-Time Degradation Computation of PV Panels

2021 ◽  
pp. 2150242
Author(s):  
Parveen Bhola ◽  
Saurabh Bhardwaj
Keyword(s):  
Root Mean Square Error ◽  
Real Time ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Meteorological Parameters ◽  
Solar Power ◽  
Performance Ratio ◽  
Accurate Estimation ◽  
Support Vector ◽  
Mean Square

Many applications including power trading and planning require the accurate estimation of solar power in real time. As the power output of the solar panels degrades over the time period, so its real-time estimation is tough without the degradation parameter. In the proposed method, the effect of degradation in terms of performance ratio is incorporated along with other meteorological parameters. The degradation is calculated in real time using the clustering-based technique without physical inspection on site. Initially, the power is estimated using Support Vector Regression (SVR) model with the meteorological parameters. The estimation is further fine-tuned in sync with the degradation rate. The model is validated on the real data (Meteorological parameters and Solar power) procured from the solar plant. After refinement, the estimation results show significant improvement in terms of statistical measures. Now, the estimation accuracy in terms of coefficient of determination R2 is 92% and the error metrics normalized root mean square error (NMRSE), mean absolute percentage error (MAPE), root mean square error (RMSE) are 7.13, 5.92 and 14.54, respectively.

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