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This paper develops a hybrid electricity price-forecasting framework to improve the accuracy of prediction. A novel clustering method is proposed that uses a modified game theoretic self-organizing map (GTSOM) and neural gas (NG) along with competitive Hebbian Learning (CHL) to provide a better vector quantization (VQ). To resolve the deficiency of the original SOM, five strategies are proposed to...
Spare parts are indispensable resources to ensure equipment the normal operation and continuous production, especially for urban raü vehicles. When the spare parts storage is insufficient, the equipment can't be replaced or repair ed in time, which can cause serious loss. Therefore, it is important to forecast the demand of the urban rail vehicle spare parts. A combination forecasting method based...
The occurrence of fire has a huge threat to people's life, therefore, in order to improve the quality of people's lives, we combines the support vector machine which is hot in recent years with the problem of home fire forecasting. Then a new method based on ensemble empirical mode decomposition and support vector machine is proposed. The results show that EEMD-SVM combination forecasting method has...
Electrical load forecasting is of great significance to guarantee the system stability under large disturbances, and optimize the distribution of energy resources in the smart grid. Traditional prediction models, which are mainly based on time series analyzing, have been unable to fully meet the actual needs of the power system, due to their non-negligible prediction errors. To improve the forecasting...
This work presents an off-line model, which can provide accurate photovoltaic forecasts for utility grid managers. For this purpose we assess the performance of two models, using well known supervised machine learning techniques, for intra-hour (15 min) solar power forecasting. The first model is created using Least Square Support Vector Regression (LS-SVR) and the second using Feed-forward Neural...
Wind speed forecasting has drawn a lot of research interests around the globe as it plays a key role in wind power plant operation. Accurate wind speed forecasting is vital for the integration of wind energy conversion system into existing electric power grids. The important factor of wind speed forecast is the choice of accurate prediction algorithm. Support Vector Machine Regression Model (SVM-R),...
Wind power prediction is very important to guarantee security and stability of the wind farm and power system operation, and wind speed forecasting is the key to wind power prediction. Due to dramatic changes and shorter collection intervals in wind speed, it generates a larger numbers of samples, which affects modeling time and accuracy. Therefore, a short-term wind speed prediction method based...
Time series forecasting has a fundamental importance in various practical domains. Many models have been proposed in literature to model and predict the Time Series Data (TSD) efficiently. As the modeling and prediction depends on the nature of TSD, one model may not be opt for all applications. This paper presents a hybrid model based on Particle Swarm Optimization (PSO) with Least Square Support...
Artificial neural network (ANN) has been widely applied in flood forecasting and got good results. However, it can still not go beyond one or two hidden layers for the problematic non-convex optimization. This paper proposes a deep learning approach by integrating stacked autoencoders (SAE) and back propagation neural networks (BPNN) for the prediction of stream flow, which simultaneously takes advantages...
As one of the machine learning methods that has been widely used in recent years, SVM can be applied to pattern classification and nonlinear regression. This paper proposes the basic modeling process by using SVM, and introduces the processing technique of dimension reduction by using MATLAB and principal component analysis method, and provides the process of classification forecasting by using SVM...
Forecasting electricity price allows market participants to make informed and sound decisions. Selecting the best training variables is often involved in forecasting in order to obtain optimal prediction. Support Vector Regression (SVR) provides an effective method to fit data and find minimal risk slack variables around a fit line. The best fit depends on the selected input feature set and the tuning...
This paper presents a proposal for the use of the Hybrid Fuzzy Inference System algorithm (HyFIS) as solar intensity forecast mechanism. Fuzzy Inference Systems (FIS) are used to solve regression problems in various contexts. The HyFIS is a method based on FIS with the particular advantage of combining fuzzy concepts with Artificial Neural Networks (ANN), thus optimizing the learning process. This...
Artificial intelligent models (AIMs) have been successfully adopted in hydrological forecasting in a plenty of literatures. However, the comprehensive comparison of their applicability in particular short-term (i.e. hourly) water level prediction under heavy rainfall events was rarely discussed. Therefore, in this study, the artificial neural networks (ANN), support vector machine (SVM) and adaptive...
Efforts have been made in financial markets to deal with price movement predicting. Recent studies have shown that the market can be outperformed by methodologies with the aid of science. In other words, it has been shown that methods based on computational intelligence can be more profitable than a buy-and-hold strategy. This paper proposes a probabilistic and dynamic chart pattern recognition hybrid...
In order to solve the shortcoming of support vector regression, passenger capacity prediction based on least squares support vector regression with ant colony optimization algorithm is proposed in this paper. Ant colony optimization algorithm is used to select the parameters of least squares support vector regression. Highway passenger capacity data of Anhui province from 2000 to 2011 are applied...
This paper investigates short term forecast of wind power generation using Support Vector Machine (SVM) methods. We propose a similar pattern matching technique for data pre-processing. The proposed technique is able to select the most appropriate segments from the available historical data for the forecasting. These segments are used for the training of parameters in the SVM model, which will then...
In the framework of a competitive commercial world, having accurate energy forecasting tools becomes a Key Performance Indicator (KPI) to the building owners. Energy forecasting plays a crucial role for any building when it undergoes the retrofitting works in order to maximize the benefits and utilities. This paper provides accurate and efficient energy forecasting tool based on Support Vector Machine...
Electrical load forecasting is an important topic within the electrical market which has been done by a machine learning methodology: Support Vector Machines (SVM). Load forecasting with SVM will form the non-linear relations with the parameters that have an effect on the load; additionally to the correct modeling of the load curve on weekends and holidays. The past information is used as a sample...
In smart power distribution systems price forecasting is an indispensable participant tool for developing purchase strategies. This paper places itself in price directed power systems, where participants respond with their load demand to incoming price signals. To that end, a two hour-ahead forecasting method using a linear composition of kernel machines for electricity prices is introduced. Initially,...
Aiming at the saturation characteristic of SVM in large sample environment, a modified SVM forecasting algorithm for wind power forecasting is proposed in this paper. The key point of the modified SVM forecasting algorithm is converting large sample set to small sample set by making classification. In this method, the optimal regression size for SVR is firstly sought out for the actual sample, and...
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