The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Seyed Mostafa Biazar - One of the best experts on this subject based on the ideXlab platform.
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Long-Term monthly average temperature Forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA
Theoretical and Applied Climatology, 2019Co-Authors: Pouya Aghelpour, Babak Mohammadi, Seyed Mostafa BiazarAbstract:Temporal changes of the global surface temperature have been used as a prominent indicator of global climate change; therefore, making dependable forecasts underlies the foundation of sound environmental policies. In this research, the accuracy of the Seasonal Autoregressive Integrated Moving Average (SARIMA) Stochastic model has been compared with the Support Vector Regression (SVR) and its merged type with Firefly optimization algorithm (SVR-FA) as a meta-innovative model, in Long-Term Forecasting of average monthly temperature. For this, 5 stations from different climates of Iran (according to the Extended De Martonne method) were selected, including Abadan, Anzali, Isfahan, Mashhad, and Tabriz. The data were collected during 1951–2011, for training (75%) and testing (25%). After selecting the best models, the average monthly temperature has been forecasted for the period 2012–2017. The results showed that the models had better performances in Extra-Arid and Warm (Abadan) and after that Extra-Arid and Cold (Isfahan) climate, in Long-Term Forecasting. The weakest performances of the models were reported in Semi-Arid and Cold climate, including Mashhad and Tabriz. Also, despite the use of the non-linear SVR model and its meta-innovative type, SVR-FA, the results showed that, in the climates of Iran, the linear and classical SARIMA model still offers a more appropriate performance in temperature Long-Term Forecasting. So that it could forecast the average monthly temperature of Abadan with root mean square error (RMSE) = 1.027 °C, and Isfahan with RMSE = 1.197 °C for the 6 years ahead. The SVR and SVR-FA models also had good performances. The results of this checking also report the effectiveness of the merging SVR model with the Firefly optimization algorithm in temperature Forecasting in Iran’s climates, so, compared with the SVR model, it is suggested to use SVR-FA for temperature Forecasting.
Vladimir M. Shiljkut - One of the best experts on this subject based on the ideXlab platform.
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Long-Term Forecasting of annual peak load considering effects of demand-side programs
Journal of Modern Power Systems and Clean Energy, 2018Co-Authors: Nikola Lj. Rajakovic, Vladimir M. ShiljkutAbstract:The main purpose of this research paper is to investigate the Long-Term effects of the proposed demand-side program, and its impact on annual peak load Forecasting important for strategic network planning. The program comprises a particular set of demand-side measures aimed at reducing the annual peak load. The paper also presents the program simulations for the case study of the Electricity Distribution Company of Belgrade (EDB). According to the methodology used, the first step is to determine the available controllable load of the distribution utility/area under consideration. The controllable load is presumed constant over the analyzed time horizon, and the smart grid (SG) infrastructure available. The saturation of positive effects during intense program application is also taken into account. Technical and economic input data are taken from the real projects. The conducted calculations indicate that demand-side programs can bring about the same results as the energy storage in the grids with a strong impact of distributed generation from variable renewable sources (V-RES). In conclusion, the proposed demand-side program is a good alternative to building new power facilities, which can postpone investment costs for a considerable period of time.
Pilar Gómez-gil - One of the best experts on this subject based on the ideXlab platform.
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Selecting and combining models with self-organizing maps for Long-Term Forecasting of chaotic time series
2014 International Joint Conference on Neural Networks (IJCNN), 2014Co-Authors: Rigoberto Fonseca-delgado, Pilar Gómez-gilAbstract:When time series are generated by chaotic systems, a reasonable estimation of large prediction horizons is hard to obtain, but this may be required by some applications. Over the last years, some researchers have focused on the use of ensembles and meta-learning as a strategy for improving prediction accuracy. This paper addresses the problem of selecting and combining models for the design of efficient Long-Term predictors of chaotic time series based on meta-learning and self-organization. We propose and evaluate the use of four heuristic rules for selecting models using a self-organizing map (SOM) neural network and meta-features. The meta-features are extracted from the performances of each involved model when applied to the training time series. A trained SOM map, which was generated using these meta-features, allows the selection of models with diverse behaviors. Two strategies for the combination of models are compared; one is based on the average and a second is based on the median of the forecasts of the selected models. The experiments were executed using four types of series: the time series dataset provided by the NN5 tournament and time series generated from the Mackey-Glass equation, from an ARIMA model and from a sine function. In most cases, the best results were obtained using a percentage of the models belonging to the group that contained the best model. Our results also showed that a combination using a median strategy obtained better results that using an average strategy.
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A Neural Network Scheme for Long-Term Forecasting of Chaotic Time Series
Neural Processing Letters, 2011Co-Authors: Pilar Gómez-gil, Juan Manuel Ramírez-cortes, Saúl E. Pomares Hernández, Vicente Alarcón-aquinoAbstract:The accuracy of a model to forecast a time series diminishes as the prediction horizon increases, in particular when the prediction is carried out recursively. Such decay is faster when the model is built using data generated by highly dynamic or chaotic systems. This paper presents a topology and training scheme for a novel artificial neural network, named “Hybrid-connected Complex Neural Network” (HCNN), which is able to capture the dynamics embedded in chaotic time series and to predict long horizons of such series. HCNN is composed of small recurrent neural networks, inserted in a structure made of feed-forward and recurrent connections and trained in several stages using the algorithm back-propagation through time (BPTT). In experiments using a Mackey-Glass time series and an electrocardiogram (ECG) as training signals, HCNN was able to output stable chaotic signals, oscillating for periods as long as four times the size of the training signals. The largest local Lyapunov Exponent (LE) of predicted signals was positive (an evidence of chaos), and similar to the LE calculated over the training signals. The magnitudes of peaks in the ECG signal were not accurately predicted, but the predicted signal was similar to the ECG in the rest of its structure.
Adrian Lucena Arnaud - One of the best experts on this subject based on the ideXlab platform.
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Continuous Dynamical Combination of Short and Long-Term Forecasts for Nonstationary Time Series
IEEE Transactions on Neural Networks and Learning Systems, 2014Co-Authors: Domingos Sávio Pereira Salazar, Paulo Jorge Leitão Adeodato, Adrian Lucena ArnaudAbstract:This brief generalizes the Forecasting method that has been awarded first-place winner in the International Competition of Time Series Forecasting (ICTSF 2012). It is based on a short-term Forecasting approach of multilayer perceptrons (MLP) ensembles, combined dynamically with a Long-Term Forecasting. The main feature of this general approach is the original concept of continuous dynamical combination of forecasts, in which the weights of the Forecasting combination are a function of forecast horizon. Experiments in ICTSFs and NN5s nonstationary time series show that this new combination method improves the performance in multistep Forecasting of MLP ensembles when compared to the MLP ensembles alone.
Pouya Aghelpour - One of the best experts on this subject based on the ideXlab platform.
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Long-Term monthly average temperature Forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA
Theoretical and Applied Climatology, 2019Co-Authors: Pouya Aghelpour, Babak Mohammadi, Seyed Mostafa BiazarAbstract:Temporal changes of the global surface temperature have been used as a prominent indicator of global climate change; therefore, making dependable forecasts underlies the foundation of sound environmental policies. In this research, the accuracy of the Seasonal Autoregressive Integrated Moving Average (SARIMA) Stochastic model has been compared with the Support Vector Regression (SVR) and its merged type with Firefly optimization algorithm (SVR-FA) as a meta-innovative model, in Long-Term Forecasting of average monthly temperature. For this, 5 stations from different climates of Iran (according to the Extended De Martonne method) were selected, including Abadan, Anzali, Isfahan, Mashhad, and Tabriz. The data were collected during 1951–2011, for training (75%) and testing (25%). After selecting the best models, the average monthly temperature has been forecasted for the period 2012–2017. The results showed that the models had better performances in Extra-Arid and Warm (Abadan) and after that Extra-Arid and Cold (Isfahan) climate, in Long-Term Forecasting. The weakest performances of the models were reported in Semi-Arid and Cold climate, including Mashhad and Tabriz. Also, despite the use of the non-linear SVR model and its meta-innovative type, SVR-FA, the results showed that, in the climates of Iran, the linear and classical SARIMA model still offers a more appropriate performance in temperature Long-Term Forecasting. So that it could forecast the average monthly temperature of Abadan with root mean square error (RMSE) = 1.027 °C, and Isfahan with RMSE = 1.197 °C for the 6 years ahead. The SVR and SVR-FA models also had good performances. The results of this checking also report the effectiveness of the merging SVR model with the Firefly optimization algorithm in temperature Forecasting in Iran’s climates, so, compared with the SVR model, it is suggested to use SVR-FA for temperature Forecasting.
