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Aiming at improving the accuracy and speed of short-term load forecasting (STLF), the proposed BCC-LS-SVM model is presented, among which bacterial colony chemotaxis (BCC) optimization algorithm is used to determine hyper-parameters of least squares support vector machine (LS-SVM). BCC is a novel category of bionic algorithm, which takes advantage of the bacterium's reaction to chemoattractants to...
Short-term electricity load forecasting is a difficult work because the accuracy of forecasting is influenced by many unpredicted factors whose relationships are commonly complex, implicit and nonlinear. By studying the methods proposed by other scholars, a mew method, ICA (independent component analysis) -LSSVM (least squares support vector machine) is proposed by this paper. The first step of this...
To obtain a better capacity of clustered Web servers, a load balancing algorithm is presented in this paper based on dual-load prediction, using the different characteristics of Web server users' requests for static and dynamic content. First, wavelet packet-SVR (support vector regression) prediction model is constructed; then, the arriving assignments are distributed according to the type of the...
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...
This paper aims to share the results on forecasting power demand using least-squares support vector machines. The development is based on model estimation taking in consideration the past measurements for power demand and ambient temperature. All approximated models were evaluated using the multiple correlation coefficient (R2) or mean absolute percentage error (MAPE) and maximum error combined as...
The Brazilian electric sector reform specifying that the remuneration of distribution utilities must be through the management of their systems increased the necessity of control and management of load flows through the connection points between their systems and the basic grid as a function of the contracted amounts. The objective of this control is to avoid that these flows exceed some thresholds...
The application of support vector machines to forecasting problems is becoming popular, lately. Several comparisons between neural networks trained with error backpropagation and support vector machines have shown advantage for the latter in different domains of application. However, some difficulties still deteriorate the performance of the support vector machines. The main one is related to the...
Aiming at improving the accuracy and speed of short-term load forecasting (STLF), the proposed BCC-LS-SVM model is presented, among which bacterial colony chemotaxis (BCC) optimization algorithm is used to determine hyper-parameters of least squares support vector machine (LS-SVM). BCC is a novel category of bionic algorithm, which takes advantage of the bacterium's reaction to chemoattractants to...
Support vector machines (SVM) has been used in load forecasting field. The noise and redundancy of sample data are important factors to the generalized performance of SVM. They can cause some disadvantages of slow convergence speed and low forecasting accuracy. A SVM forecasting method for short-term load forecasting based on rough sets (RS-SVM) is developed in this paper, using rough sets algorithm...
The combination forecasting is the trend of practical load forecast. However, its weight is relatively difficult to set. DS theory can choose optimal combined weights according to historical prediction results and avoid the subjective influence which has great significance. By analyzing historical errors of the signal model and ruling the homologous basic belief degree function, the combined weights...
This paper proposes a new probabilistic method for maximum temperature forecasting in short-term electrical load forecasting. The proposed method makes use of Gaussian process (GP)of the kernel machine to evaluate the predicted temperature. In recent years, electric power markets become more deregulated and competitive. The power system players are concerned with maximizing a profit while minimizing...
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...
Support Vector Machine (SVM) is a type of learning machine which has been proved to be available in solving the problems of nonlinear regression. The decision of SVM parameters is essential. In this paper a new SVM model based on particle swarm optimization (PSO) for parameter optimization has been proposed. PSO algorithm has extensive capability of global optimization. Once the PSO finds the optimal...
Accurate forecasting of electricity load has been one of the most important issues in the electricity industry. Modern data mining methods have played a crucial role in forecasting electricity load. Support vector machines (SVMs) have been successfully employed to solve nonlinear regression and time series problems. Based on the Nystro??m approximation and the primal-dual formulation of the least...
Accurate short term load forecasting plays a very important role in power system management. As electrical load data is highly non-linear in nature, in the proposed approach, we first separate out the linear and the non-linear parts, and then forecast the load using the nonlinear part only. The semi-parametric spectral estimation method is used to decompose a load data signal into a harmonic linear...
This paper deals with the application of a least squares support vector machine (LS-SVM) in short-time load forecasting (STLF). The objective of this paper is to examine the feasibility of SVM in STLF by comparing it with a artificial neural network (ANN). The experiment shows that LS-SVM outperforms the ANN based on the criteria of mean absolute error (MAE), mean absolute percent error (MAPE), mean...
Mid-long term load is affected by many factors, it is difficult to forecast by a single method. This paper analyzes the advantages and disadvantages of grey forecasting method and least squares support vector machine (LS-SVM) respectively, proposes a new forecasting model of grey least squares support vector machine which develops the advantages of accumulation generation in the grey forecasting method,...
The paper presents a new model of Short-term load forecasting based on pattern-base. It can be described as follows: firstly, it recognizes the different patterns of daily load according such features as weather and date type by means of data mining technology of classification and regression tree; secondly, it sets up pattern-bases which are composed of daily load data sequence with highly similar...
Mid-long term load forecasting (MTLF) plays an important role in power system. With more factors involved, single forecasting method becomes hard to satisfy requirement. This paper proposes a new combination model for MTLF based on least squares support vector machines (LS-SVM) and particle swarm optimization (PSO) algorithm. LS-SVM is a new kind of SVM which regresses faster than standard, and a...
In electric power system, long term peak load forecasting plays an important role in terms of policy planning and budget allocation. The planning of power system expansion project starts with the forecasting of anticipated load requirement. Accurate forecasting method can be helpful in developing power supply strategy and development plan, especially for developing countries where the demand is increased...
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