Electricity Peak Load Demand using De-noising Wavelet Transform integrated with Neural Network Methods

This paper presents architecture of backpropagation Artificial Neural Network (ANN) and Support Vector Regression (SVR) models in supervised learning process for cement demand dataset. This study aims to identify the effectiveness of each parameter of mean square error (MSE) indicators for time series dataset. The study varies different random sample in each demand parameter in the network of ANN and support vector function as well. The variations of percent datasets from activation function, learning rate of sigmoid and purelin, hidden layer, neurons, and training function should be applied for ANN. Furthermore, SVR is varied in kernel function, lost function and insensitivity to obtain the best result from its simulation. The best results of this study for ANN activation function is Sigmoid. The amount of data input is 100% or 96 of data, 150 learning rates, one hidden layer, trinlm training function, 15 neurons and 3 total layers. The best results for SVR are six variables that run in optimal condition, kernel function is linear, loss function is ౬-insensitive, and insensitivity was 1. The better results for both methods are six variables. The contribution of this study is to obtain the optimal parameters for specific variables of ANN and SVR.

Section: Test Of Network Training Functionmentioning

confidence: 85%

Initial Optimal Parameters of Artificial Neural Network and Support Vector Regression

Fradinata¹,

Suthummanon²,

Suntiamorntut³

2018

“…The application of the Box-Jenkins ARIMA model is developed through a seasonal pattern [16]- [22].For models based on artificial intelligence has also become the attention of researchers as in [23]- [27]. The hybrid model has also been developed to obtain the best data in electrical load prediction study as in [28]- [36].…”

Section: Introductionmentioning

confidence: 99%

Applying of Double Seasonal ARIMA Model for Electrical Power Demand Forecasting at PT. PLN Gresik Indonesia

Mado

Soeprijanto

Suhartono

2018

The prediction of the use of electric power is very important to maintain a balance between the supply and demand of electric power in the power generation system. Due to a fluctuating of electrical power demand in the electricity load center, an accurate forecasting method is required to maintain the efficiency and reliability of power generation system continuously. Such conditions greatly affect the dynamic stability of power generation systems. The objective of this research is to propose Double Seasonal Autoregressive Integrated Moving Average (DSARIMA) to predict electricity load. Half hourly load data for of three years period at PT. PLN Gresik Indonesia power plant unit are used as case study. The parameters of DSARIMA model are estimated by using least squares method. The result shows that the best model to predict these data is subset DSARIMA with order ([1,2,7,16,18,35,46],1,[1,3,13,21,27,46])(1,1,1)48(0,0,1)336 with MAPE about 2.06%. Thus, future research could be done by using these predictive results as models of optimal control parameters on the power system side.

“…In another hand, some researchers who have implemented the hybrid method between wavelet and NN, or hybrid among machine learning methods for time series forecasting ie Bunnoon [22] has forecasted the electricity peak load demand, Poorani and Murugan [23] have forecasted the rising demand for electric vehicles applicable to Indian road conditions, Kamley, et al [24] have measured the performance forecasting of the share market, and the enabling external factors for inflation rate forecasting were conducted by Sari, et al [25]. In the previous hybrid methods that were not a hybrid between wavelet and RBFNN.…”

Section: Introductionmentioning

confidence: 99%

“…In the previous hybrid methods that were not a hybrid between wavelet and RBFNN. Both in Burnoon [22], and in Poorani&Murugan [23] combined between wavelet and FFNN, meanwhile both in Kamley, et al [24] and in Sari, et al [25] combined between NN, and fuzzy inferences system. Furthermore, modeling the hybrid between wavelet and RBFNN is focus on this research.…”

Section: Introductionmentioning

confidence: 99%

System for Prediction of Non Stationary Time Series based on the Wavelet Radial Bases Function Neural Network Model

Kusdarwati¹,

Handoyo²

2018

This paper proposes and examines the performance of a hybrid model called the wavelet radial bases function neural networks (WRBFNN). The model will be compared its performance with the wavelet feed forward neural networks (WFFN model by developing a prediction or forecasting system that considers two types of input formats: input9 and input17, and also considers 4 types of non-stationary time series data. The MODWT transform is used to generate wavelet and smooth coefficients, in which several elements of both coefficients are chosen in a particular way to serve as inputs to the NN model in both RBFNN and FFNN models. The performance of both WRBFNN and WFFNN models is evaluated by using MAPE and MSE value indicators, while the computation process of the two models is compared using two indicators, many epoch, and length of training. In stationary benchmark data, all models have a performance with very high accuracy. The WRBFNN9 model is the most superior model in nonstationary data containing linear trend elements, while the WFFNN17 model performs best on non-stationary data with the non-linear trend and seasonal elements. In terms of speed in computing, the WRBFNN model is superior with a much smaller number of epochs and much shorter training time.