18,273 research outputs found

    Fish swarmed Fuzzy Time Series for Photovoltaic’s Forecasting in Microgrid

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    Forecasting irradiation and temperature is important for designing photovoltaic systems because these two factors have a significant impact on system performance. Irradiation refers to the amount of solar radiation that reaches the earth's surface, and directly affects the amount of energy that can be generated by a photovoltaic system. Therefore, accurate irradiation forecasting is essential for estimating the amount of energy a photovoltaic system can produce, and can assist in determining the appropriate system size, configuration, and orientation to maximize energy output. Temperature also plays an important role in the performance of a photovoltaic system. With increasing temperature, the efficiency of the solar cell decreases, which means that the energy output of the system also decreases. Therefore, accurate temperature forecasts are essential for estimating system energy output, selecting suitable materials, and designing effective cooling systems to prevent overheating. In summary, forecasting irradiation and temperature is important for designing photovoltaic systems as it helps in determining suitable system size, configuration, orientation, material selection, and cooling system, which ultimately results in higher energy output and better system performance. In recent decades, many forecasting models have been built on the idea of fuzzy time series. There are several forecasting models proposed by integrating fuzzy time series with heuristic or evolutionary algorithms such as genetic algorithms, but the results are not satisfactory. To improve forecasting accuracy, a new hybrid forecasting model combines fish swarm optimization algorithm with fuzzy time series. The results of irradiance prediction/forecasting with the smallest error are using the type of Fuzzy Time Series prediction model optimized with FSOA with RMSE is 0.83832

    Evolving Time Series Forecasting Neural Network Models

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    In the last decade, bio-inspired methods have gained an increasing acceptation as alternative approaches for Time Series Forecasting. Indeed, the use of tools such as Artificial Neural Networks (ANNs) and Genetic and Evolutionary Algorithms (GEAs), introduced important features to forecasting models, taking advantage of nonlinear learning and adaptive search. In the present approach, a combination of both paradigms is proposed, where the GEA's searching engine will be used to evolve candidate ANNs topologies, enhancing forecasting models that show good generalization capabilities. A comparison was performed, contrasting bio-inspired and conventional methods, which revealed better forecasting performances, specially when more difficult series were taken into consideration; i.e., nonlinear and chaotic ones.The work of Paulo Cortez was supported by the portuguese Foundation of Science & Technology through the PRAXIS XXI/BD/13793/97 grant. The work of José Neves was supported by the PRAXIS' project PRAXIS/P/EEI/13096/98

    An evolutionary artificial neural network time series forecasting system

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    Artificial Neural Networks (ANNs) have the ability of learning and to adapt to new situations by recognizing patterns in previous data. Time Series (TS) (observations ordered in time) often present a high degree of noise which difficults forecasting. Using ANNs for Time Series Forecasting (TSF) may be appealing. However, the main problem with this approach is on the search for the best ANN architecture. Genetic Algorithms (GAs) are suited for problems of combinatorial nature, where other methods seem to fail. Therefore, an integration of ANNs and GAs for TSF, taking the advantages of both methods, may be appealing. ANNs will learn to forecast by back-propagation. Different ANNs architectures will give different forecasts, leading to competition. At the end of the evolutionary process the resulting ANN is expected to return the best possible forecast. It is asserted that the combined strategy exceeded conventional TSF methods on TS of high non-linear degree, particularly for long term forecasts

    Modelling and trading the Greek stock market with gene expression and genetic programing algorithms

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    This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in the form of mathematical expressions, decision trees or logical expressions. The results indicate that GEP and GP produce significant trading performance when applied to ASE 20 and outperform the well-known existing methods. The trading performance of the derived models is further enhanced by applying a leverage filter

    Time series forecasting by means of evolutionary algorithms

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    IEEE International Parallel and Distributed Processing Symposium. Long Beach, CA, 26-30 March 2007Many physical and artificial phenomena can be described by time series. The prediction of such phenomenon could be as complex as interesting. There are many time series forecasting methods, but most of them only look for general rules to predict the whole series. The main problem is that time series usually have local behaviours that don't allow forecasting the time series by general rules. In this paper, a new method for finding local prediction rules is presented. Those local prediction rules can attain a better general prediction accuracy. The method presented in this paper is based on the evolution of a rule system encoded following a Michigan approach. For testing this method, several time series domains have been used: a widely known artificial one, the Mackey-Glass time series, and two real world ones, the Venice Lagon and the sunspot time series

    Predicting expected TCP throughput using genetic algorithm

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    Predicting the expected throughput of TCP is important for several aspects such as e.g. determining handover criteria for future multihomed mobile nodes or determining the expected throughput of a given MPTCP subflow for load-balancing reasons. However, this is challenging due to time varying behavior of the underlying network characteristics. In this paper, we present a genetic-algorithm-based prediction model for estimating TCP throughput values. Our approach tries to find the best matching combination of mathematical functions that approximate a given time series that accounts for the TCP throughput samples using genetic algorithm. Based on collected historical datapoints about measured TCP throughput samples, our algorithm estimates expected throughput over time. We evaluate the quality of the prediction using different selection and diversity strategies for creating new chromosomes. Also, we explore the use of different fitness functions in order to evaluate the goodness of a chromosome. The goal is to show how different tuning on the genetic algorithm may have an impact on the prediction. Using extensive simulations over several TCP throughput traces, we find that the genetic algorithm successfully finds reasonable matching mathematical functions that allow to describe the TCP sampled throughput values with good fidelity. We also explore the effectiveness of predicting time series throughput samples for a given prediction horizon and estimate the prediction error and confidence.Peer ReviewedPostprint (author's final draft

    Forecasting time series by means of evolutionary algorithms

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    Proceeding of: 8th International Conference in Parallel Problem Solving from Nature - PPSN VIII , Birmingham, UK, September 18-22, 2004.The time series forecast is a very complex problem, consisting in predicting the behaviour of a data series with only the information of the previous sequence. There is many physical and artificial phenomenon that can be described by time series. The prediction of such phenomenon could be very complex. For instance, in the case of tide forecast, unusually high tides, or sea surges, result from a combination of chaotic climatic elements in conjunction with the more normal, periodic, tidal systems associated with a particular area. Too much variables influence the behaviour of the water level. Our problem is not only to find prediction rules, we also need to discard the noise and select the representative data. Our objective is to generate a set of prediction rules. There are many methods tying to achieve good predictions. In most of the cases this methods look for general rules that are able to predict the whole series. The problem is that usually the time series has local behaviours that dont allow a good level of prediction when using general rules. In this work we present a method for finding local rules able to predict only some zones of the series but achieving better level prediction. This method is based on the evolution of set of rules genetically codified, and following the Michigan approach. For evaluating the proposal, two different domains have been used: an artificial domain widely use in the bibliography (Mackey-Glass series) and a time series corresponding to a natural phenomenon, the water level in Venice Lagoon.Investigation supported by the Spanish Ministry of Science and Technology through the TRACER project under contract TIC2002-04498-C05-
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