Mathematical Modelling and Short-term Forecasting of Electricity Consumption of the Power System, with Due Account of Air Temperature and Natural Illumination, Based on Support Vector Machine and Particle Swarm

Nadtoka, Ivan; Balasim, M. Al-Zihery

doi:10.1016/j.proeng.2015.12.087

Cited by 25 publications

(15 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various variants of artificial neural networks were implemented to model complex and nonlinear relationships between features used for forecasting and achieved high accuracies (Agrawal et al, 2019). The non-linear relationship model in the complex structure of electricity demand allows being overcome by ANN (Nadtoka & Al-Zihery Balasim, 2015).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Linear Regressions and Neural Networks for Forecasting Electricity Consumption

Setiyorini

Frieyadie

2020

pilar

View full text Add to dashboard Cite

Electricity has a major role in humans that is very necessary for daily life. Forecasting of electricity consumption can guide the government's strategy for the use and development of energy in the future. But the complex and non-linear electricity consumption dataset is a challenge. Traditional time series models in such as linear regression are unable to solve nonlinear and complex data-related problems. While neural networks can overcome the problems of nonlinear and complex data relationships. This was proven in the experiments in this study. Experiments carried out with linear regressions and neural networks on the electricity consumption dataset A and the electricity consumption dataset B. Then the RMSE results are compared on the linear regressions and neural networks of the two datasets. On the electricity consumption dataset, A obtained by RMSE of 0.032 used the linear regression, and RMSE of 0.015 used the neural network. On the electricity consumption, dataset B obtained by RMSE of 0.488 used the linear regression, and RMSE of 0.466 used the neural network. The use of neural networks shows a smaller RMSE value compared to the use of linear regressions. This shows that neural networks can overcome nonlinear problems in the electricity consumption dataset A and the electricity consumption dataset B. So that the neural networks are afforded to improve performance better than linear regressions. This study to prove that there is a nonlinear relationship in the electricity consumption dataset used in this study, and compare which performance is better between using linear regression and neural networks.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparison of Linear Regressions and Neural Networks for Forecasting Electricity Consumption

Setiyorini

Frieyadie

2020

pilar

View full text Add to dashboard Cite

show abstract

“…Another way to classify the related work is the mathematical techniques used to forecasting, for example, Artificial Neural Networks (ANN) [8,9,27], Bagged Regression Trees (BRT) [8], Support Vector Machines (SVM) [9,28], Multiple Regression Models (Linear and Non-linear) [10,14,17,19,23,30], Genetic Algorithms (GA) [12], Fuzzy [18,29], Simulation [20], Decision Tree [24], Particle Swarm Optimization [28,29].…”

Section: Related Workmentioning

confidence: 99%

Forecasting Electricity Consumption Using Weather Data in an Edge-Fog-Cloud Data Analytics Architecture

Olivares-Rojas

Reyes-Archundia

Gnecchi

et al. 2020

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

The forecasting of electricity consumption is a well-study research problem; however, electricity consumption is a complex model because it depends on many factors, and its accuracy is not always accurate. The accuracy of this forecasting impact; for example, in the utilities in the bulk generation of electricity and in the end-user at economical prices. This work shows the implementation of a forecasting model considering weather data across the smart metering system infrastructure using and edge-fog-cloud architecture for data analytics. The results show that using weather data across edge-fog-cloud architecture is an excellent alternative to forecast electricity consumption.

show abstract

“…Vladimir proposed one of the most famous artificial intelligent learning machines and named it as Support Vector Machines (SVM) [18]. Basically, the SVM optimization equations can be expressed as [13]:…”

Section: A Svmmentioning

confidence: 99%

“…Xing et al suggested many hybrid prediction methods based on SVM, and one of the methods depend on the use of least squares support vector machine [12]. The PSO-SVM was developed by many researchers such as: Nadtoka I for short forecasting [13,14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Short-Term Load Forecasting for Electrical Dispatcher of Baghdad City based on SVM-FA

Jaber¹,

Satar²,

Shalash³

2018

ijacsa

View full text Add to dashboard Cite

The improvement of load forecasting accuracy is an important issue in the scientific optimization of power systems. The availability of accurate statistical data and a suitable scientific method are necessary for a perfect prediction of future occurrences. This research deals with the use of a regression forecast model (Support Vector Machine, SVM) for the prediction of the vector data for electrical power loading and temperature in Baghdad city. The Firefly algorithm was used to optimize the parameters of the SVM to improve its prediction accuracy. The quantitative statistical performance evaluation measures (absolute proportional error (MAPE)) were used to evaluate the performance of the optimization methods. The results proved that the modification method was more accurate compared to the basic method and PSO-SVM.

show abstract

Mathematical Modelling and Short-term Forecasting of Electricity Consumption of the Power System, with Due Account of Air Temperature and Natural Illumination, Based on Support Vector Machine and Particle Swarm

Cited by 25 publications

References 8 publications

Comparison of Linear Regressions and Neural Networks for Forecasting Electricity Consumption

Comparison of Linear Regressions and Neural Networks for Forecasting Electricity Consumption

Forecasting Electricity Consumption Using Weather Data in an Edge-Fog-Cloud Data Analytics Architecture

Short-Term Load Forecasting for Electrical Dispatcher of Baghdad City based on SVM-FA

Contact Info

Product

Resources

About