228 research outputs found

    Exogenous Measurements from Basic Meteorological Stations forWind Speed Forecasting

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    This research presents a comparative analysis of wind speed forecasting methods applied to perform 1 h-ahead forecasting. The main significant development has been the introduction of low-quality measurements as exogenous information to improve these predictions. Eight prediction models have been assessed; three of these models [persistence, autoregressive integrated moving average (ARIMA) and multiple linear regression] are used as references, and the remaining five, based on neural networks, are evaluated on the basis of two procedures. Firstly, four quality indices are assessed (the Pearson’s correlation coefficient, the index of agreement, the mean absolute error and the mean squared error). Secondly, an analysis of variance test and multiple comparison procedure are conducted. The findings indicate that a backpropagation network with five neurons in the hidden layer is the best model obtained with respect to the reference models. The pair of improvements (mean absolute-mean squared error) obtained are 29.10%–56.54%, 28.15%–53.99% and 4.93%–14.38%, for the persistence, ARIMA and multiple linear regression models, respectively. The experimental results reported in this paper show that traditional agricultural measurements enhance the predictions

    Comparison of Adaptive Holt-Winters Exponential Smoothing and Recurrent Neural Network Model for Forecasting Rainfall in Malang City

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    Rainfall forecast is necessary for many aspects of regional management. Prediction of rainfall is useful for reducing negative impacts caused by the intensity of rainfall, such as landslides, floods, and storms. Hence, a rainfall forecast with good accuracy is needed. Many rainfall forecasting models have been developed, including the adaptive Holt-Winters exponential smoothing method and the Recurrent Neural Network (RNN) method. The research aimed to compare the result of forecasting between the Holt-Winters adaptive exponential smoothing method and the Recurrent Neural Network (RNN) method. The data were monthly rainfall data in Malang City from January 1983 to December 2019 obtained from a website. Then, the data were divided into training data and testing data. Training data consisted of rainfall data in Malang City from January 1983 to December 2017. Meanwhile, the testing data were rainfall data in Malang City from January 2018 to December 2019. The comparison result was assessed based on the values of Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The result reveals that the RNN method has better RMSE and MAPE values, namely RMSE values of 0,377 and MAPE values of 1,596, than the Holt-Winter Adaptive Exponential Smoothing method with RMSE values of 0,500 and MAPE values of 0,620. It can be concluded that the non-linear model has better forecasting than the linear model. Therefore, the RNN model can be used in modeling and forecasting trend and seasonal time series

    Big Data Analysis application in the renewable energy market: wind power

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    Entre as enerxías renovables, a enerxía eólica e unha das tecnoloxías mundiais de rápido crecemento. Non obstante, esta incerteza debería minimizarse para programar e xestionar mellor os activos de xeración tradicionais para compensar a falta de electricidade nas redes electricas. A aparición de técnicas baseadas en datos ou aprendizaxe automática deu a capacidade de proporcionar predicións espaciais e temporais de alta resolución da velocidade e potencia do vento. Neste traballo desenvólvense tres modelos diferentes de ANN, abordando tres grandes problemas na predición de series de datos con esta técnica: garantía de calidade de datos e imputación de datos non válidos, asignación de hiperparámetros e selección de funcións. Os modelos desenvolvidos baséanse en técnicas de agrupación, optimización e procesamento de sinais para proporcionar predicións de velocidade e potencia do vento a curto e medio prazo (de minutos a horas)

    Dynamic non-linear system modelling using wavelet-based soft computing techniques

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    The enormous number of complex systems results in the necessity of high-level and cost-efficient modelling structures for the operators and system designers. Model-based approaches offer a very challenging way to integrate a priori knowledge into the procedure. Soft computing based models in particular, can successfully be applied in cases of highly nonlinear problems. A further reason for dealing with so called soft computational model based techniques is that in real-world cases, many times only partial, uncertain and/or inaccurate data is available. Wavelet-Based soft computing techniques are considered, as one of the latest trends in system identification/modelling. This thesis provides a comprehensive synopsis of the main wavelet-based approaches to model the non-linear dynamical systems in real world problems in conjunction with possible twists and novelties aiming for more accurate and less complex modelling structure. Initially, an on-line structure and parameter design has been considered in an adaptive Neuro- Fuzzy (NF) scheme. The problem of redundant membership functions and consequently fuzzy rules is circumvented by applying an adaptive structure. The growth of a special type of Fungus (Monascus ruber van Tieghem) is examined against several other approaches for further justification of the proposed methodology. By extending the line of research, two Morlet Wavelet Neural Network (WNN) structures have been introduced. Increasing the accuracy and decreasing the computational cost are both the primary targets of proposed novelties. Modifying the synoptic weights by replacing them with Linear Combination Weights (LCW) and also imposing a Hybrid Learning Algorithm (HLA) comprising of Gradient Descent (GD) and Recursive Least Square (RLS), are the tools utilised for the above challenges. These two models differ from the point of view of structure while they share the same HLA scheme. The second approach contains an additional Multiplication layer, plus its hidden layer contains several sub-WNNs for each input dimension. The practical superiority of these extensions is demonstrated by simulation and experimental results on real non-linear dynamic system; Listeria Monocytogenes survival curves in Ultra-High Temperature (UHT) whole milk, and consolidated with comprehensive comparison with other suggested schemes. At the next stage, the extended clustering-based fuzzy version of the proposed WNN schemes, is presented as the ultimate structure in this thesis. The proposed Fuzzy Wavelet Neural network (FWNN) benefitted from Gaussian Mixture Models (GMMs) clustering feature, updated by a modified Expectation-Maximization (EM) algorithm. One of the main aims of this thesis is to illustrate how the GMM-EM scheme could be used not only for detecting useful knowledge from the data by building accurate regression, but also for the identification of complex systems. The structure of FWNN is based on the basis of fuzzy rules including wavelet functions in the consequent parts of rules. In order to improve the function approximation accuracy and general capability of the FWNN system, an efficient hybrid learning approach is used to adjust the parameters of dilation, translation, weights, and membership. Extended Kalman Filter (EKF) is employed for wavelet parameters adjustment together with Weighted Least Square (WLS) which is dedicated for the Linear Combination Weights fine-tuning. The results of a real-world application of Short Time Load Forecasting (STLF) further re-enforced the plausibility of the above technique

    An Optimized Classification Algorithm by Neural Network Ensemble Based on PLS and OLS

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    Using the neural network to classify the data which has higher dimension and fewer samples means overmuch feature inputs influence the structure design of neural network and fewer samples will generate incomplete or overfitting phenomenon during the neural network training. All of the above will restrict the recognition precision obviously. It is even better to use neural network to classify and, therefore, propose a neural network ensemble optimized classification algorithm based on PLS and OLS in this paper. The new algorithm takes some advantages of partial least squares (PLS) algorithm to reduce the feature dimension of small sample data, which obtains the low-dimensional and stronger illustrative data; using ordinary least squares (OLS) theory determines the weights of each neural network in ensemble learning system. Feature dimension reduction is applied to simplify the neural network’s structure and improve the operation efficiency; ensemble learning can compensate for the information loss caused by the dimension reduction; on the other hand, it improves the recognition precision of classification system. Finally, through the case analysis, the experiment results suggest that the operating efficiency and recognition precision of new algorithm are greatly improved, which is worthy of further promotion

    Flood Forecasting Using Machine Learning Methods

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    This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

    Application of Recurrent Neural Networks for Drought Projections in California

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    We use recurrent neural networks (RNNs) to investigate the complex interactions between the long-term trend in dryness and a projected, short but intense, period of wetness due to the 2015-2016 El Niño. Although it was forecasted that this El Niño season would bring significant rainfall to the region, our long-term projections of the Palmer Z Index (PZI) showed a continuing drought trend, contrasting with the 1998-1999 El Niño event. RNN training considered PZI data during 1896-2006 that was validated against the 2006-2015 period to evaluate the potential of extreme precipitation forecast. We achieved a statistically significant correlation of 0.610 between forecasted and observed PZI on the validation set for a lead time of 1 month. This gives strong confidence to the forecasted precipitation indicator. The 2015-2016 El Niño season proved to be relatively weak as compared with the 1997-1998, with a peak PZI anomaly of 0.242 standard deviations below historical averages, continuing drought conditions

    Simulation of carbon peaking process of high energy consuming manufacturing industry in Shaanxi Province: A hybrid model based on LMDI and TentSSA-ENN

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    To achieve the goals of carbon peaking and carbon neutrality in Shaanxi, the high energy consuming manufacturing industry (HMI), as an important contributor, is a key link and important channel for energy conservation. In this paper, the logarithmic mean Divisia index (LMDI) method is applied to determine the driving factors of carbon emissions from the aspects of economy, energy and society, and the contribution of these factors was analyzed. Meanwhile, the improved sparrow search algorithm is used to optimize Elman neural network (ENN) to construct a new hybrid prediction model. Finally, three different development scenarios are designed using scenario analysis method to explore the potential of HMI in Shaanxi Province to achieve carbon peak in the future. The results show that: (1) The biggest promoting factor is industrial structure, and the biggest inhibiting factor is energy intensity among the drivers of carbon emissions, which are analyzed effectively in HMI using the LMDI method. (2) Compared with other neural network models, the proposed hybrid prediction model has higher accuracy and better stability in predicting industrial carbon emissions, it is more suitable for simulating the carbon peaking process of HMI. (3) Only in the coordinated development scenario, the HMI in Shaanxi is likely to achieve the carbon peak in 2030, and the carbon emission curve of the other two scenarios has not reached the peak. Then, according to the results of scenario analysis, specific and evaluable suggestions on carbon emission reduction for HMI in Shaanxi are put forward, such as optimizing energy and industrial structure and making full use of innovative resources of Shaanxi characteristic units

    Short-Term Forecasting of Wind Speed and Wind Power Based on BP and Adaboost_BP

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    Due to wind is intermittent and less dispatchable, wind power fluctuates as the wind fluctuating and is uncontrollable. Therefore, when wind power accounts for a higher proportion of total electricity generation of the system, power generation plan needs to arrange according to the variation of wind power output. The way to solve the problem is forecasting the wind power. In this paper, we focus on the wind speed and wind power forecasting in the time scales of 10min, 1h and 3h in the future. BP Neural Network and Adaboost_BP Neural Network are selected as the forecasting model for wind speed forecasting. And for wind power forecasting model, we use BP Neural Network as the method. The wind farm which the data used in this paper comes from has 31 wind turbines in the same type. As the geographical distribution of this wind farm is unknown, we pick one No.17 wind turbine as the optimal one by analyzing the data of 31 wind turbines and making the curve fitting of wind speed and wind power. Then we analyze the influencing factor of wind power, and find out wind speed as the most influential factor. For the wind speed, we deal with the raw data which selected from SCADA (Supervisory Control and Data Acquisition) system before using it. For the wind speed forecasting model, we find the optimal number of the training data for each training sets for the BP Neural Network in each time scale. Then we make a contrast of the accuracy of the single-step forecasting accuracy between BP Neural Network model and Adaboost_BP model in 10min, 1h and 3h at their respectively optimal number of the training data. And there is a comparison between the accuracy of the single-step and iterative multi-step wind speed forecasting model in 1h and 3h time scales at the number of the training data of 10 for the two models. For the wind power forecasting model, we use the forecasting wind speed and its corresponding wind power to build the input matrix for the network training. And we find that not only the number of the training data for each training sets but also the range of wind power affects the forecasting accuracy. Then make a contrast of the the wind power forecasting result with forecasting wind speed and actual wind speed as the input
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