Ronaldo R. B. de Aquino

Application of wavelet and neural network models for wind speed and power generation forecasting in a Brazilian experimental wind park

The wind speed and wind generation forecasting are of extreme importance to aid in the planning studies and scheduled operation of hydrothermal and wind systems. This kind of generation is in the incipient phase in Brazil; however, the perspectives are mainly exciting aiming for increasing the potential of electricity generation. The use of wind power for producing electricity can create uncertainties in the generation. Therefore, the development of wind forecasting models is essential to integrate this kind of energy source with the generation system in an effective way. This work proposes the application of Artificial Neural Networks - ANN to produce a tool capable of accomplishing the wind speed forecasting. The ANN model is created using input data preprocessing by the Wavelet Transform - WT to extract important characteristics of the wind speed. Outputs of several ANNs show clearly the potential of the model based on WT compared with the others.

Recurrent neural networks solving a real large scale mid-term scheduling for power plants

This paper deals with an application of artificial neural network (ANN) to solve the operation planning problem of generation systems in the mid-term operation horizon. This problem is related to economic power dispatch that minimizes the overall production cost while satisfies the load demand. These kinds of problem are large scale optimization problems in which the complexity increases with the planning horizon and the accuracy of the system to be modeled. This paper considers the two-phase optimization neural network which solves linear and quadratic programming problems. These networks are based on the solution of a set of differential equations that are obtained from a transformation of an augmented Lagrange energy function. This network also provides the corresponding Lagrange multiplier associated with each constraint which is the marginal price. The results indicate that the developed ANN model provides optimal scheduling of hydro, thermal and wind power plant towards the minimal operation cost.

A fuzzy system for detection of incipient fault in power transformers based on gas-in-oil analysis

Dissolved gas analysis (DGA) is one of the most useful techniques do detect the incipient faults of power transformer. However, the identification of the faulted location by the traditional method is not always an easy task due to the variability of gas data and operational natures. This work aims to develop an intelligent system of preventive maintenance for automatically detecting incipient fault in power transformers through analysis of dissolved gases in transformer insulating oil by introducing the IEC 599 standard (International Electrotechnical Commission). The data used to model the system are taken from chromatographic analysis of CELPE (Electrical Company from Pernambuco). The results show a system with high accuracy when compared with other papers approaching the same problem. Moreover, the results also proved the ability of multiple incipient faults detection.

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.

SAX-quantile based multiresolution approach for finding heatwave events in summer temperature time series

Time series pattern discovery is of great importance in a large variety of environmental and engineering applications, from supporting predictive models to helping to understand hidden underlying processes. This work develops a multiresolution time series method for extracting patterns in weather records, particular temperature data. The topic is important, as, given a warming climate, morbidity and mortality are expected to rise as heatwave frequency and intensity increase. By analysing summer temperature quantiles at different levels of coarseness, it was found that compounding models can contain a complete description of severe weather events. This new multiresolution quantile approach is developed as an extension of the symbolic aggregate approximation of the temperature time series in which quantiles are computed at every stretch of the piecewise partition. The process is iterated at different scales of the partition, and it was found to be a very useful approach for finding patterns related to both heatwave periods and intensities. The method is successfully tested using real weather records from Brazil (Recife) and the UK (London), and it was found that in both locations heatwave intensity and frequency are increasing at a substantial rate. In addition, it was found that the rate of increase in intensity of the heatwaves is far outstripping the rate of increase in mean summer temperature: by a factor of 2 in Recife and a factor of 6 in London. The approach will be of use to those looking at the impact of future climates on civil engineering, water resources, energy use, agriculture and health care, or those looking for sustained extreme events in any time series.