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Electrical load forecasting is essential in the field of power systems to enhance the operation and economical utilization In this paper, a combined approaches of artificial neural networks (ANN) with particle-swarm-optimization (PSO) and genetic algorithm optimization (GA) for short and mid-term load forecasting is developed. The model identifies the relationship among load, temperature and humidity...
This paper studies data-driven short-term load forecasting, where historic data are used to predict the expected load for the next 24 h. Our focus is to simplify and automate the estimation and analysis of various forecasting models. We propose a three-stage approach to load forecasting, consisting of preprocessing, forecasting, and postprocessing, where the forecasting stage uses evolution to automatically...
Management and pricing of electricity in power system is largely influenced by Short-Term Load Forecasting (STLF). This paper presents a hybrid algorithm, where Radial Basis Function Neural Network (RBFNN) is optimized using Genetic Algorithm (GA) for STLF, with load and day-type as input parameters. Since, conventional training methods, viz., principle component analysis and least square method,...
Electric load forecasting plays a critical role for the reliable and efficient operation of power grids. In this paper we propose a load forecasting model using parallel radial basis function neural networks (RBFNN). The proposed implementation of RBFNN allows parallel computation therefore expedites the convergence of training process. The proposed model also employs a new hybrid chaotic genetic...
To improve the precision of middle and long-term power load forecast, combined forecasting method has been introduced in this Paper. The advantages of several forecasting methods can he integrated in the model to get more exact results.But because of the characteristic of the optimal combination model, it can not be solved directly by LP algorithm. The genetic algorithm has been used to get the combination...
The wide-spread integration of renewable energies in modern power systems is a vital pre-requisite to transform the global energy system towards sustainability. The very obstacle that prevents these sources from spreading is its intermittent nature which results in a fluctuating generated power profile and that considerably affects the ability of these supplies to satisfy the required demand independently...
Support vector machines (SVM), which are based on statistical learning theory and structural risk minimization principle, according to limited sample information, search the best compromise between the model complexity and the learning ability, and have good prediction effect. However, in the methods of load forecasting which are based on SVM, the choices of penalty coefficient c, insensitive coefficient...
Accurate short-term wind speed forecasting is very important to improve the security and stability of power grid and to reduce the running cost. In this paper, a method based on Least squares support vector machine (LS-SVM) was proposed to the short-term forecasting. In order to avoid the blindness and inaccuracy of Parameter selection, Genetic algorithm is used to select the optimal regularization...
Wind turbine power output is totally intermittent in the nature. For grid connected wind turbine generators, power system operators (transmission system operators) need reliable and robust wind power forecasting system. Rapid changes in the wind generation relative to the load require proper energy management system to maintain the power system stability and of course to balance the power generation,...
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...
Prediction of village electrical load is very important to manage village electrical load efficiently. Support vector regression (SVR) is a new learning algorithm based on statistical learning theory, which has a good time-series forecasting ability. As the choice of the best parameters of support vector regression is an important problem for support vector regression, and this problem will directly...
In this article, we propose a nonlinear forecasting model based on radial basis function neural networks (RBF-NNs) with Gaussian activation functions and robust clustering algorithms to model the conditional mean and a parametric generalized autoregressive conditional heteroskedasticity (GARCH) specification to model the conditional volatility. Instead of calibrating the parameters of the RBF-NNs...
The rapid growth of wind generation is introducing additional variability and uncertainty into power system operations and planning. These inherent characteristics of wind power have both technical and commercial implications for efficient planning and operation of power systems. As the penetration of wind power increases, the importance of accurate forecasting of this variable generation source over...
Traditional time series forecasting models are difficult to capture the nonlinear patterns. Support vector regression (SVR) is a powerful tool for modeling the inputs and output(s) of complex and nonlinear systems. However, parameters determination for a SVR model is competent to the forecasting accuracy. Several evolutionary algorithms, such as genetic algorithms and simulated annealing algorithms...
Based on SVM (Support Vector Machine) theory, and the model to predict air conditioning load was established. In order to optimize the behavior of SVM, the DE (Differential Evolution) algorithm was introduced into classic SVM. The DE-SVM model is applied to a real example. The comparisons between the predicted results of the three models-GA (Genetic Algorithm) model, ACO (Ant Colony Optimization)...
The least square support vector machines (LS-SVM) is applied to solve the practical problems of less samples and non-linear prediction better, and it is suitable for the forecasting dissolved gas in transformer oil. But in this model, the selecting values of the parameters impacts on the results of the diagnosis greatly, so it is necessary to optimize these parameters. A new method to optimize these...
The traditional gray forecasting model is widely used in various fields, but it has some limitations. In this paper, a method based on genetic algorithm optimizing gray modeling process is introduced, and the flow chart of modeling is given. This method makes full use of the advantages of the gray forecasting model and characteristics of genetic algorithm to find global optimization. So the forecasting...
This study develops a novel methodology hybridizing genetic algorithms (GAs) and support vector regression (SVR) and implements this model in a problem forecasting hourly cooling load. The aim of this study is to examine the feasibility of SVR in building cooling load forecasting by comparing it with back-propagation neural networks (BPNN) and the autoregressive integrated moving average (ARIMA) model...
The increasing importance and complexity of STLF necessitates more accurate load forecast methods. A novel genetic algorithm (GA) based support vector machine (SVM) forecasting model with determinstic annealing (DA) clustering is presented in this paper. For NN forecasting, too many training data may lead to long training time and slow convergent speed. First deterministic annealing (DA)for load data...
Accurate electricity price forecasting can provide crucial information for electricity market participants to make reasonable competing strategies. Support vector machine (SVM) is a novel algorithm based on statistical learning theory, which has greater generalization ability, and is superior to the empirical risk minimization principle as adopted by traditional neural networks. However, its generalization...
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