Electrical Load Forecasting

A multi-layer feed forward neural network model for accurate prediction of flue gas sulfuric acid dew points in process industries

Flash floods can demolish infrastructure and property within seconds as they are very sudden. Flash floods are the main cause of the casualties and loss of properties. Existing natural disaster prediction algorithms contains false alarms. Indefinite techniques have been applied to overcome this leading issue in many countries. A competent flood management system must have the potential and tendency to identify the flash floods and atmospheric and climatic changes on early basis with less false alarm rate. Techniques which have been designed for the flash flood investigation may be categorized into following types a. Sensors based direct measurement b. Radar images c. Satellite based X-band images. The proposed research consisted of Artificial intelligence-based decision making for multi-modal sensing (direct measurement from multi-resolution sensors). A combination of sensors like Passive infrared (PIR), water level sensor, ultrasonic sensor, temperature sensor, pressure and altimeter sensors have been integrated on a single device to investigate the flash floods. The use of most suitable pair of measurement sensors can substantially enhance the advantage of more accuracy and reliability compared to a single sensor. In recent trends Particle swarm optimization is very popular for solving stochastic global optimization problems. The data was trained and processed by modified multi-layer feed forward neural network optimized by particle swarm optimization algorithm. Hybrid Modified Particle swarm optimization has been combined with feed forward neural network for the vigorous investigation of flash floods with less false alarm rate. Simulated results showed that the proposed research algorithm Modified multi-layer feed forward neural network optimized by Particle swarm optimization for multi-modal sensing performed very well in terms of evaluation parameters compared to other existing strategies with minimum false alarm ratio. Moreover, modified multi-layer feed forward neural network optimized by article swarm optimization algorithm results have been compared with the cuckoo search, modified cuckoo search, particle swarm optimization and Multi-layer perceptron neural network configurations for the validation purpose.

Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach

A multilayer feedforward neural network with a modification to the standard backpropagation training of neural nets is considered. Information on the gradients of target outputs with respect to network inputs is used in this modification. This modified neural network is then applied for approximating design requirements of aerospace composite components, such as an aircraft engine guide vane and a satellite reflector assembly. This neural network functional approximator is then used with an optimization algorithm for determining the optimal parameters for component design requirements. The modified neural network provides for faster convergence of learning process and requires a smaller number of hidden layer nodes for convergence to similar training error levels. HE application of neural networks as universal approximators has been the focus of tremendous amount of activity in the past few years.16 Neural networks have been used for approximating and modeling both memoryless systems and dynamical systems with memory. This paper does not go into the various neural net architectures and training schemes, and the interested reader could refer to standard texts on this subject matter.1'2 This paper considers static neural networks (systems without memory) formed by interconnections without loops, called feedforward networks.2 In addition, this work considers a backpropagation learning scheme with adaptation of learning rate. There have been many additions to, or adaptations of, the basic backpropagation scheme, such as adaptive learning rate, learning with momentum, and the like,1'2 for improving convergence of the learning scheme, and these are not addressed in this paper. This paper explores a modification of the backpropagation learning scheme using information on the gradient of the target outputs with respect to the inputs of the network. The performance of the modified learning scheme is compared with that of the standard backpropagation learning scheme.2 A feedforward network with a single hidden layer trained with this modified learning scheme is applied in structural optimization problems. Figure 1 shows a feedforward net with a single hidden layer. The restriction to a single hidden layer is not a limitation of the modified learning scheme, and the new training scheme can be used on networks with multiple hidden layers also. The use of neural networks for structural response approximation has been reported by several authors.46 The primary reason for investigating such properties is to assess the applicability of trained neural networks for quick and accurate approximation of response functions during structural design optimization. Structural optimization, utilizing either gradient-based numerical search or genetic search algorithms, requires repeated function (response) evaluations during the search process. A single finite element analysis of large-scale structures requires a significant amount of computational time and resource and hence the need for robust approxima

This paper employs a feed‐forward neural network with a back‐propagation algorithm for the short‐term electric load forecasting of daily peak (valley) loads and hourly loads in the northern areas of Vietnam. A large set of data on peak loads, valley loads, hourly loads and temperatures was used to train and calibrate the artificial neural network (ANN). The calibrated network was used for load forecasting. The mean percentage errors for the peak load, valley load, one‐hour‐ahead hourly load and 24‐hour‐ahead hourly load were −1.47%, −3.29%, −2.64% and −4.39%, respectively. These results compare well with similar studies.

Interframe principal feature extraction using a multi-layer feedforward neural network

Inflow data for longer length at a reservoir site is necessary for proper planning and operation of the reservoir. However presently for most of the reservoirs, the measured length of inflow data is insufficient for use in planning and operation. Artificial neural networks (ANNs) have been applied within the field of hydrological modeling for over a decade but relatively little attention has been paid to the use of these tools for flood estimation in catchments. Modeling of non-linearity and uncertainty associated with rainfall-runoff process has received a lot of attention in the past years. We analyzed the potential of neural network models for the estimation of inflow for Yuvacik Dam Catchment. Multilayer feed-forward neural networks were developed to model the relationships between known rain, snow depth and temperature data. Results suggest that artificial neural network model can be simple, robust, reliable and a cost-efficient tool for environmental inflow determination at the catchment area.

An increasing number of applications of dynamic neural networks has been developed for digital signal processing (DSP), dynamic neural networks are feedforward neural networks with commonly used scalar synapses replaced by linear filters. This provides feedforward neural networks with the capability of performing dynamic mappings, which depend on past input values, dynamic neural networks are suitable for time series prediction, nonlinear system identification, and signal processing applications. Their most popular types are Finite Impulse Response (FIR) neural networks, which are obtained by replacing synapses with finite impulse response filters. Due to their guaranteed stability characteristic and easy to minimize error surface they have been used with great success in many applications such as signal enhancement, noise cancellation, classification of input patterns, system identification, prediction, and control.. Most of the works on system identification using neural networks are based on multilayer feedforward neural networks with backpropagation learning or more efficient variations of this algorithm, an elegant method for training layered networks. This paper is based on work in a Dynamic System Modeling (DYSMO) and as an application for speed control of DC motor drive.

Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm

To develop and evaluate a model for obstructive sleep apnea (OSA) detection using an artificial neural network (ANN) based on the combined features of body mass index (BMI), electrocardiogram (ECG), and pulse oxygen saturation (SpO2). Polysomnography (PSG) data for 148 patients withOSA and 33 unaffected individuals were included. A multi-layer feed-forward neural network (FNN) was used based on the features obtained from ECG, SpO2, and BMI. The receiver operating characteristic (ROC) curve and the metrics of accuracy, sensitivity, and specificity were used to evaluate the performance of the overall classification. Some other machine learning methods including linear discriminant, linear Support Vector Machine (SVM), Complex Tree, RUSBoosted Trees, and Logistic Regression were also used to compare their performance with the FNN. The accuracy, sensitivity, and specificity of the proposed multi-layer FNN were 97.8%, 98.6%, and 93.9%, respectively, and the area under the ROC curve was 97.0%. Compared with the other machine learning methods mentioned above, the FNN achieved the highest performance. The satisfactory performance of the proposed FNN model for OSA detection indicated that it is reliable to screen potential patients withOSA using the combined channels of ECG and SpO2 and also taking into account BMI. This strategy might be a viable alternative method for OSA diagnosis.

This paper is concerned with optical character recognition (OCR) system for Bangla conjunctive characters. A method is proposed giving emphasis on the identification of the characters using the proposed methodology. Here generalization is achieved by preprocessing the characters before presenting them to the system for classification. The pre-processing systematically functions isolating the characters from BMP images, as well as noise removal, scaling and binary image conversion. The method uses a flexible matching between sample data and training data components applying Multi-layered Feed-forward Artificial Neural Network. A number of parameters of Neural Network are estimated so that the system performance is improved in comparison with normal training. Classification of character is defined as error minimization among the possible training set. Also, a measure of the amount of distortion for this training is given. Application of Artificial Neural Network in character recognition has made the system faster with optimum performance.

Summary form only given. An analytic method for constructing polynomial functions by multi layer feedforward neural networks has been developed. Because the polynomials consist of multiplication operations and linear weighted summations, if the multiplier can be constructed by a neural network, any polynomial function can be represented by a neural network (a single unit already has the function of weighted summation). An attempt has been made to construct a neural network module with one hidden layer that works as a multiplier. It was shown that the multiplier can be approximated by a neural network with four hidden units, with arbitrary accuracy on a bounded closed set. >

A greenhouse control with feed-forward and recurrent neural networks

This paper addresses the problem of fitting a functional model to data corrupted with outliers using a multilayered feed-forward neural network. The importance of this problem stems from the vast, diverse, practical applications of neural networks as data-driven function approximator or model estimator. Yet, the challenges raised by the presence of outliers in the data have not received the same careful attention from the neural network research community. The paper proposes an enhanced algorithm to train neural networks for robust function approximation in a random sample consensus (RANSAC) framework. The new algorithm follows the same strategy of the original RANSAC algorithm, but employs an M-estimator cost function to decide the best estimated model. The proposed algorithm is evaluated on synthetic data, contaminated with varying degrees of outliers, and compared to existing neural network training algorithms.

This paper presents neural network-based dimension reduction of texture features in content-based image retrieval. In particular, we highlight the usefulness of hetero-associative neural networks to this task, and also propose a scheme to combine the hetero-associative and auto-associative functions. A multichannel Gabor-filtering approach is used to derive 30-dimensional texture features from a set of homogeneous texture images. Multi-layer feedforward neural networks are then trained to reduce the number of feature dimensions. Our results show that the methods lead to a reduction of up to 30% while keeping or even improving the performance of similarity ranking. This has the benefit of alleviating the ill-effects of the high dimensionality of features in current image indexing methods and resulting in significant speeding up retrieval rates. Results using principal component analysis are also provided for comparison.