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An essential element of electric utility resource planning is the long term forecast of the electricity consumption. This paper presents an approach to forecast annual electricity consumption by using artificial neural network based on historical data for Malaysia. It involves developing several ANN designs and selecting the best network that can produce the best results in terms of its accuracy....
In this paper, an automatic quality inspection system for the riveting process by using quantum neural network (QNN) was proposed. This inspection system not only can monitor the real time riveting process, but also can give the assistance on the riveting quality verification. For demonstrating the superiority of the inspection system we developed, the data provided by the experiment did by Chinese...
Artificial Neural Networks (ANN's) have large number of difficulties such as large training time, local minima error, large data etc. Generalized Neuron Model (GNM) overcomes the above drawbacks which specifically uses summation (Σ) and product (π). In this paper, the prediction of GNM to apply for Short Term Load Forecasting with different error functions with the results of root mean square error,...
In this paper a new algorithm is proposed for Short Term Load Forecasting (STLF) using Echo State Networks (ESN). Hourly load data along with only average temperature of each day and day type flag is fed to the ESN and nonlinear mapping is done using training methods. Despite conventional recurrent neural networks, ESN can be trained much easier and with great deal of accuracy. Simulation results...
This paper presents the artificial neural network (ANN) that used to perform the short-term load forecasting (STLF). The input data of ANN is comprises of multiple lags of hourly peak load. Hence, imperative information regarding to the movement patterns of a time series can be obtained based on the multiple time lags of chronological hourly peak load. This may assist towards the improvement of ANN...
This paper presents the non-stationary power signal forecasting by using a neural network with modified neurons for PJM data set provided by Independent Electricity System Operator (IESO). In this data set, the load information is the sum of power load consumed by three areas, including Allentown, Baltimore and Philadelphia. The historical load and temperature information from year 2003 to year 2008...
In this paper, a new non-iterative state estimation based neural network is proposed for solving short term load forecasting of distribution systems. In this approach, the weights between the layers of neural network have been estimated using the weighted least square state estimation (WLSSE) technique without any iterative approach. The WLSSE technique could offer well established weights by accounting...
A novel clustering based Short Term Load Forecasting (STLF) using Artificial Neural Network (ANN) for forecasting the next day load is presented in this paper. The input parameters considered for prediction are load, temperature and day of the week. The daily average load of each day for all the training patterns and testing patterns is calculated and the patterns are clustered using a threshold value...
Recent researches in load forecasting are quite often based on the use of neural networks in order to predict a specific variable (maximum demand, active electric power or hourly consumption) using past values of the same variable and other exogenous factors proved to influence the value being predicted. This work aims to explore different input patterns in neural networks incorporating information...
A novel clustering based short term load forecasting (STLF) using artificial neural network (ANN) to forecast the 48 half hourly loads for next day is presented in this paper. The proposed architecture uses the historical load and temperature to forecast the next day load. It is trained using back propagation algorithm and tested. The daily average load of each day for all the training patterns and...
The paper addresses the problem of predicting hourly load demand using adaptive artificial neural networks (ANNs). A particle swarm optimization (PSO) algorithm is employed to adjust the network's weights in the training phase of the ANNs. The advantage of using a PSO algorithm over other conventional training algorithms such as the back-propagation (BP) is that potential solutions will be flown through...
A new hybrid technique using support vector machines (SVM) to forecast the next `24' hours load is proposed in this paper. Four modules consisting of the basic SVM, peak and valley SVM, averager and forecaster and adaptive combiner form the integrated method for load forecasting. The proposed architecture can forecast the next `24' hours load. The basic SVM uses the historical data of load and temperature...
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