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In this study, a novel two-step hourly solar radiation modelling procedure is developed. In the first step, hourly solar radiation data of a single day is considered and its periodic Fourier series coefficients are calculated. The first nine Fourier series coefficients are considered to accurately render the data. Among these nine coefficients, the yearly variations of the first two coefficients exhibit...
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 order to solve the problem with being easily trapped in a local optimum of back propagation neural network (BPNN) and the premature convergence based on standard genetic algorithm (SGA), a dynamic and adaptive model which combines the modified genetic algorithm (MGA) with BPNN is proposed in this paper. By introducing modified genetic operators and dynamic mutation probability measure, the MGA-BP...
This paper presents a comparison of data mining techniques for wind power forecasting in a time frame out to 15 minutes ahead. The forecasting is focused on the power generated by the wind farms and the power changes are predicted by using multivariate time series models ARMA, focus time-delay neural network (FTDNN) and a phenomenological model of the turbines. All these models are tested with real...
Very short-term load forecasting predicts the load over one hour into the future in five-minute steps and performs the moving forecast every five minutes. To quantify forecasting accuracy, the confidence interval is estimated in real-time. An effective prediction with a small associated confidence interval is important for area generation control and resource dispatch, and can help the operator further...
In view of the power load with the randomicity and the complexity, the short-term power load forecasting based on optimal wavelet-particle swarm is introduced in this paper. First, the power load series is decomposed several frequency ranges by wavelet packet. Select the optimal wavelet tree to reconstruct the coefficients of the wavelet packet and form the number of power load components. Then, forecast...
Time series forecasting is an important aspect of dynamic data analysis and processing, in science, economics, engineering and many other applications there exists using the historical data to predict the problem of the future, and is one considerable practical value of applied research. Time series forecasting is an interdisciplinary study field, this paper is under the guidance of the introduction...
Time series analysis has been applied intensively and sophisticatedly to model and forecast many problems in the biological, physical and environmental phenomena of interest. This fact accounts for the basic engineering problem in forecasting the daily peak system load to use time series analysis. ARMA and REgARMA models are among the times series models considered. ANFIS, a hybrid model from neural...
In the analysis of predicting power load forecasting based on least squares neural network, the instability of the time series could lead to decrease of prediction accuracy. On the other hand,neural network and chaos theories parameters must be carefully predetermined in establishing an efficient model. In order to solve the problems mentioned above, in this paper, the neural network and chaos theory...
A new combined BP neural network model based on accelerating genetic algorithm is put forward in this paper. On the foundation of traditional BP neural network, this method is given better iteration values improved by accelerating genetic algorithm, thus and increase iteration rate and avoid sinking into local minimum. Then, it is applied to forecast the heat load in a certain area, and compared with...
Short-term load forecasting is important for electricity load planning and dispatches the loading of generating units in order to meet the electricity system demand. The precision of the load forecasting is related to electricity company's economic. This paper presents a approach named an autoregressive moving average (ARMA) cooperate with BP Artificial Neural Network (BPNN) approach, which can combine...
In order to improve the load-forecast precision and availability of power system, a method based on Elman neural network and MATLAB is presented to create a load forecast model, which according to the Elman neural network model having the characteristics of approach to arbitrary nonlinear functions and having the ability of reflecting the dynamic behavior of the system and for the practicability and...
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...
The power load forecasting precision being influenced by many factors, the traditional forecasting tools are not very taking the role. In fact, BP network has the characteristics of the applicable and self-learning, and grey method has the growth characteristics,this paper used the correcting coefficient to improved the grey method, so the grey BP network method can better reflect the increasing and...
A. new forecasting model based on HHT and combination of ANN is proposed in the paper. Load data can be decomposed into several IMF components and remainder by EMD firstly. Through calculating the spectrum of decomposed series by Hilbert transform algorithm, we can choose one appropriate forecasting model for each low frequency component, while use combination of ANN model for the high frequency component,...
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...
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...
To improve the accuracy of power load forecasting, this paper analyzes the defects as well as merits of artificial neural network (ANN) and grey prediction method, and it combines the two methods to propose a novel forecasting method called grey neural network (GNN). GNN utilizes the accumulation generation operation (AGO) of grey prediction to transform the original load data to first order AGO data...
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