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Electricity is one of not only the most necessities for the daily life activities of people, but also the major driving force for economic growth and development of every country. Due to the unstorable nature of electricity, the adequate supply of electricity has to be always available and uninterruptible to meet the intermittently growing demand. This paper is proposed Neural Networks (NN) with Backpropagation...
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....
The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques to solve different engineering problems. Besides, Short Term Electrical Load Forecasting (STLF) is one of the important concerns of power systems and accurate load forecasting is vital for managing supply and demand of electricity. This study estimates short term electricity loads...
We use autocorrelation analysis to extract 6 nested feature sets of previous electricity loads for 5-minite ahead electricity load forecasting. We evaluate their predictive power using Australian electricity data. Our results show that the most important variables for accurate prediction are previous loads from the forecast day, 1, 2 and 7 days ago. By using also load variables from 3 and 6 days ago,...
The paper proposed to use artificial neural networks (ANNs) to forecast electricity demand and to build the climate sensitive model. Digital simulation studies were conducted on the New Zealand electricity demand with temperature variations. The simulation study results have shown that the changes in temperature have a big impact on the expected total demand of electricity. As a result, it can be...
A prediction scheme of electric load using a Bilinear Recurrent Neural Network (BRNN) is proposed in this paper. Since the BRNN is based on the bilinear polynomial, BRNN has been successfully used in modeling highly nonlinear systems with time-series characteristics. Dynamic BRNN further improves the convergence of BRNN and the Dynamic BRNN can be a natural candidate in predicting electric load. The...
To settle the problem which the precision and generalization performance of forecast model is affected easily by input variable, the method which reconstructs the original input space of back-propagation neural network by principal component analysis that can eliminate the relevance of value is researched. The method can not only reduce duplicated information but also extract the leading factors....
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...
In this paper, we expand previous work and present an accurate electricity load forecasting algorithm with back propagation neural networks. It contributes to short-term electricity load forecast methodology with neural network with weather feature such as max centigrade, min centigrade and weather types. The original electricity load is from shanghai district, which is composed of original every...
Wind energy is rapidly emerging as a substantial contributor to the electricity generation capacity of utilities around the world. While the use of wind power both adds to the electricity supply and offers significant environmental benefits as a renewable source of energy, the stochastic nature of forces that produce wind energy prevents relying on it to meet base load requirements. Intermittent availability...
Electrical load forecasting is one of the important concerns of power systems and has been studied from different views. Electrical load forecast might be performed over different time intervals of short, medium and long term. Various techniques have been proposed for short term, medium term or long term load forecasting. In this study we employ artificial neural networks (ANN) and regression (linear...
Accurate models for electricity power load forecasting are essential to the operation and planning of an electricity company. Neural Networks are considered as a computational model that is capable of doing nonlinear curve fitting. In this research, the application of neural networks to study the design of Short Term load Forecasting (STLF) Systems for Sri Lanka was explored. Three layered neural...
According to the low sample and multifactor impact for long-medium term power load forecasting, the grey relational grade was used in screening factors, the combined model in BP neural network and SVM was established, and the multivariate variables and history load variables were used to roll prediction. The combined predictive values are obviously better than single method. Empirical study showed...
An improved BP Neural Network with additional momentum and adaptive learning is proposed in the paper to predict the growth rate of electricity consumption in China. Matlab7 is used as modeling tool to design the model. Current year GDP growth, electric power consumption growth and growth rate of secondary industry are taken as input variables while next year electric power consumption growth is predicted...
According to the low sample and multifactor impact for long-medium term power load forecasting, the grey relational grade was used in screening factors, the combined model in BP neural network and SVM was established, and the multivariate variables and history load variables were used to roll prediction. The combined predictive values are obviously better than single method. Empirical study showed...
Today, it's the need of developed and developing countries to consume electricity more efficiently. Though developed countries do not want to waste electricity and developing countries cannot waste electricity. Hence, the wise use of electricity is the need of hour. This leads to the concept - load forecasting. This paper is written for the short term load forecasting on daily basis. Though this can...
By combining a cluster of microCHP appliances, a virtual power plant can be formed. To use such a virtual power plant, a good heat demand prediction of individual households is needed since the heat demand determines the production capacity. In this paper we present the results of using neural networks techniques to predict the heat demand of individual households. This prediction is required to determine...
Accurate forecasting of short-term electricity load has been one of the most important issues in the electricity industry. And the forecasting accuracy is influenced by many unpredicted factors. Artificial neural network is a novel type of learning method, which has been successfully employed to solve nonlinear regression and time series problems. In this paper, it is proposed a new optimal model...
Neural networks (NN) have been widely used for electricity forecasting, but some difficulties are still found. One of those difficulties is in choosing the optimal network parameter, which are strongly important to obtain accurate result. ldquoTrial and errorrdquo commonly used to set the parameter is ineffective in terms of processing time and the accuracy. In this paper, Taguchi method is employed...
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