Aida A. Ferreira

Genetic algorithm for reservoir computing optimization

This paper presents reservoir computing optimization using Genetic Algorithm. Reservoir Computing is a new paradigm for using artificial neural networks. Despite its promising performance, Reservoir Computing has still some drawbacks: the reservoir is created randomly; the reservoir needs to be large enough to be able to capture all the features of the data. We propose here a method to optimize the choice of global parameters using genetic algorithm. This method was applied on a real problem of time series forecasting. The time of search for the best global parameters with GA was just 22.22% of the time- consuming task to an exhausting search of the same parameters.

Forecasting models of wind power in Northeastern of Brazil

Wind Power forecasting is extremely important to assist in planning and programming studies for the operation of wind power generation. Several studies have shown that the Brazilian wind potential can contribute significantly to the electricity supply, especially in the Northeast Brazil, where winds present an important feature of being complementary in relation to the flows of the San Francisco River. However, using wind power to generate electricity has some drawbacks, such as uncertainties in generation and some difficulty in planning and operation of the power system. This paper presents actual results of wind power forecasting for two parks in the region of northeastern Brazil with four different models. Models that perform power generation forecasting using the forecasted wind speeds and the wind power curve of the park are called Wind to Power (W2P) and models that perform power generation forecasting using the historical power generation of the park are called Power to Power (P2P). The models perform forecasting of wind power generation with 6 hours ahead, discretized by 10 minutes and with 5 days ahead, discretized by 30 minutes. Models that directly predict the wind power (P2P) got the best results. These models were more suitable for use in the power systems operation planning considering the wind parks analyzed in northeastern Brazil.

Wind forecasting and wind power generation: Looking for the best model based on artificial intelligence

Wind forecasting is extremely important to assist in planning and programming studies for the operation of wind power generation. Several studies have shown that the Brazilian wind potential can contribute significantly to the electricity supply, especially in the Northeast, where winds present an important feature of being complementary in relation to the flows of the San Francisco River. However, using wind power to generate electricity has some drawbacks, such as uncertainties in generation and some difficulty in planning and operation of the power system. This work proposes and develops models to forecast hourly average wind speeds and wind power generation based on Artificial Neural Networks, Fuzzy Logic and Wavelets. The models were adjusted for forecasting with variable steps up to twenty-four hours ahead. The gain of some of the developed models in relation to the reference models was of approximately 80% for forecasts in a period of one hour ahead. The results showed that a wavelet analysis combined with artificial intelligence tools provides more reliable forecasts than those obtained with the reference models, especially for forecasts in a period of 1 to 6 hours ahead.

Using genetic algorithm to develop a neural-network-based load forecasting

This work uses artificial neural networks, whose architecture were developed using genetic algorithm to realize the hourly load forecasting based on the monthly total load consumption registered by the Energy Company of Pernambuco (CELPE). The proposed Hybrid Intelligent System - HIS was able to find the trade-off between forecast errors and network complexity. The load forecasting produces the essence to increase and strengthen in the basic grid, moreover study into program and planning of the system operation. The load forecasting quality contributes substantially to indicating more accurate consuming market, and making electrical system planning and operating more efficient. The forecast models developed comprise the period of 45 and 49 days ahead. Comparisons between the four models were achieved by using historical data from 2005.

Comparing recurrent networks for time-series forecasting

This paper provides a comparison between two methods for time series forecasting. The first method is based on traditional recurrent neural networks (RNNs) while the second method is based in Reservoir Computing (RC). Reservoir Computing is a new paradigm that offers an intuitive methodology for using the temporal processing power of RNNs without the inconvenience of training them. So we decided to compare the advantages / disadvantages of using Reservoir Computing and RNNs in the problem of time series forecasting. The first method uses a Nonlinear Autoregressive Network with exogenous inputs (NARX). Optimization was carried out on the NARX architecture through an optimization procedure focused on the best mean squared error (MSE) metrics in the training set. The second method, called RCDESIGN, combines an evolutionary algorithm with Reservoir Computing and simultaneously looks for the best values of parameters, topology and weight matrices without rescaling the reservoir by the spectral radius. Nevertheless RCDESIGN has yielded fast tracking and excellent performance in some benchmark problems including the Narma and Mackey-Glass time-series.

Combined artificial neural network and adaptive neuro-fuzzy inference system for improving a short-term electric load forecasting

The main topic in this work was the development of a hybrid intelligent system for the hourly load forecasting in a time period of 7 days ahead, using a combination of Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System. The hourly load forecasting was accomplished in two steps: in the first one, two ANNs are used to forecast the total load of the day, where one of the networks forecasts the working days (Monday through Friday), and the other forecasts the Saturdays, Sundays and public holidays; in the second step, the ANFIS was used to give the hourly consumption rate of the load. The proposed system presented a better performance as against the system currently used by Energy Company of Pernambuco, named PREVER. The simulation results showed an hourly mean absolute percentage error of 2.81% for the year 2005.

Combining artificial neural networks and heuristic rules in a hybrid intelligent load forecast system

In this work, an Artificial Neural Network (ANN) is combined to Heuristic Rules producing a powerful hybrid intelligent system for short and mid-term electric load forecasting. The Heuristic Rules are used to adjust the ANN output to improve the system performance. The study was based on load demand data of Energy Company of Pernambuco (CELPE), which contain the hourly load consumption in the period from January-2000 until December-2004. The more critical period of the rationing in Brazil was eliminated from the data file, as well as the consumption of the holidays. For this reason, the proposed system forecasts a holiday as one Saturday or Sunday based on the specialists information. The result obtained with the proposed system is compared with the currently system used by CELPE to test its effectiveness. In addition, it was also compared to the result of the ANN acting alone.

Using Reservoir Computing for Forecasting Time Series: Brazilian Case Study

This paper presents a Brazilian case study of forecasting a wind speed time series with reservoir computing (RC). RC is a research area, in which an untrained recurrent network of nodes is used for the recognition of temporal patters. In RC only the weights of the connections in a linear output layer are trained. This reduces the complexity of recurrent neural networks (RNN) training to simple linear regression. In this work we used echo state network (ESN) to create the case study and compare the results with Multilayer Perceptron Networks and persistence method. Our case study concerns forecasting the wind speed, which is fundamental information in the operation planning for electrical wind power systems. The results showed that the RC performed significantly better than multilayer perceptron networks or persistence method, even though it presents a significantly simpler and faster, training algorithm.

Investigating the use of Echo State Networks for prediction of wind power generation

This paper presents the results of models created for prediction of wind power generation using Echo State Networks (ESN). An echo state network consist of a large, randomly connected neural network, the reservoir, which is driven by an input signal and projects to output units. ESN offer an intuitive methodology for using the temporal processing power of recurrent neural networks without the hassle of training them. The models perform forecasting of wind power generation with 6 hours ahead, discretized by 10 minutes and with 5 days ahead, discretized by 30 minutes. These models use ESNs with spectral radius greater than 1 and even then they can make predictions with good results. The forecast horizons presented here fall in medium-term forecasts, up to five days ahead, which is an appropriate horizon to subsidize the operation planning of power systems. Models that directly predict the wind power generation with ESNs showed promising results.

Evolutionary strategy for simultaneous optimization of parameters, topology and reservoir weights in Echo State Networks

Reservoir Computing is a new paradigm in artificial recurrent neural network training. A reservoir is generated randomly and only a readout layer is training [1]. Its simplicity and ease of use, paired with its underlying computational power make it an ideal choice for many application domains, for example time-series prediction, speech recognition, noise modeling, dynamic pattern classification, reinforcement learning and language modeling. However it is necessary to adjust the parameters and the topology to create a “good” reservoir for a given application. This paper presents an original investigation of an evolutionary method for simultaneous optimization of parameters, topology and reservoir weights in Echo State Networks. Optimizing reservoirs is a challenge and several evolutionary strategies for optimizing reservoirs have been presented, generally using the idea of separating the topology and reservoir weights to reduce the search space [1]. Here we present a method to optimize everything in concert. The results of this method applied to two different time series are shown and conferred with previous works.