Power Generation Forecasting of Dual-Axis Solar Tracked PV System Based on Averaging and Simple Weighting Ensemble Neural Networks

Solar power is a renewable energy interest many researchers around the world to be explored for human life beneficial especially for electric power generation. Photovoltaic (PV) is one of technology developed massively to exploit the solar power for this purpose. However, its performance is very sensitive to environmental condition such as solar irradiance, weather, and climatic behavior. Thus, the hybrid power generation systems are developed to solve this output uncertainty problem. To support this such hybrid system, this paper proposes an ensemble neural network based forecaster of the power output of PV systems which will lead an efficient power management. The object of this research is the PV systems equipped with two axes automated solar tracking with peak power 10Wp. The proposed ensemble forecaster model employs four multi-layer perceptron neural networks with two hidden layers as base forecasters while the input number of historical data is varied in order to exploit the forecaster diversity. The final prediction is calculated both by conventional averaging and simple weighting optimized by the least square fitting technique. According to the research results, the both proposed approaches provide low error rate. Moreover, in term of comparison, the ensemble model with averaging combining technique gives the highest accuracy comparing to the other ensemble and conventional neural network structures

Hyperparameters optimization on neural networks for bond trading

Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementArtificial Neural Networks have been recently spotlighted as de facto tools used for classification. Their ability to deal with complex decision boundaries makes them potentially suitable to work on trading within financial markets, namely on Bonds. Such classifier faces high flexibility on its parameters in parallel with great modularity of its techniques, arising thus the need to efficiently optimize its hyperparameters. To determine the most effcient search method to optimize almost the majority of the Neural Networks hyperparameters, we have compared the results obtained by the manual, evolutionary (genetic algorithm) and random search methods. The search methods compete on several metrics from which we aim to estimate the generalization capability, i.e. the capacity to correctly predict on unseen data. We have found the manual method to present better generalization results than the remaining automatic methods. Also, no benefit was found on the direction provided by the genetic search method when compared to the purely random. Such results demonstrate the importance of human oversight during the hyperparameters optimization and weight training phases, capable of analyzing in parallel multiple metrics and data visualization techniques, a process critical to avoid suboptimal solutions when navigating complex hyperspaces

Previsão da inflação através de wavelets e redes neurais

A inflação é caracterizada pelo aumento dos preços contínuo e generalizado em uma economia. Pequenas taxas de inflação são naturais e associadas com o crescimento saudável de uma economia; entretanto, a incerteza relacionada com a volatilidade e com a previsibilidade da inflação traz dificuldades na manutenção do poder de compra e, em nível macroeconômico, no delineamento de políticas monetárias. Para descrever o comportamento persistente da inflação, vários bancos centrais utilizam os Núcleos de Inflação, cujo objetivo é decompor a inflação em componentes persistentes e transitórios a fim de observar a sua tendência. Neste trabalho, propõe-se núcleos de inflação baseados em wavelets, utilizando as famílias Daubechies e Symlets, os quais permitem a exclusão de componentes transitórios sem serem necessárias hipóteses adicionais. Para construção desses núcleos, foi utilizado o Índice de Preços ao Consumidor Amplo(IPCA), no período entre julho de 2006 a dezembro de 2019. Uma série de testes são aplicados aos núcleos propostos com a finalidade de comparar estes com os atualmente adotados pelo Banco Central do Brasil. Ainda, com objetivo de prever o comportamento futuro da inflação brasileira, adotam-se técnicas de inteligência artificial, como as redes neurais. Ressalta-se que o uso das redes neurais possibilita lidar com problemas altamente complexos, os quais nem sempre podem ser descritos por modelos analíticos. Delimitam-se, ainda, as estimativas prováveis das previsões futuras através de intervalos de confiança. Dentre as principais conclusões do trabalho, salienta-se que os núcleos de inflação baseados em wavelets possuem menores erros de previsão para horizontes mais breves, até seis meses. Além disso, verifica-se que a previsão gerada pelos núcleos de inflação são suavizações da inflação, permitindo identificar apenas a tendência da inflação para um horizonte de até doze meses.Inflation is the continuous and generalized increase in prices in an economy. Small inflation rate is natural and associated with a healthy growth of an economy, however, uncertainties related to inflation volatility and predictability bring issues to maintain purchasing power and, at the macroeconomic level, in monetary policy design. To describe inflation persistent behavior, several central banks use core inflation to decompose inflation in persistent and transient components and observe its tendency. In this work, we proposed wavelet core inflation using Daubechies and Symlet families, which allow transient component exclusion without the need of additional hypothesis. For these cores, we use the ´Indice de Pre¸cos ao Consumidor Amplo(IPCA), between july 2006 and dezember 2019. Numerous tests were applied to the proposed core inflation in order to compare these with those currently used by the Central Bank of Brazil. Moreover, in order to forecast the future behavior of Brazilian inflation, we use artificial intelligence techniques, such as neural networks. We point out that neural networks make it possible to deal with highly complex problems, which cannot always be described by analytical models. Additionally, we use confidence intervals to delimit inflation forecast probable estimates. Among the main conclusions, we emphasize that wavelet inflation core had fewer errors for shorter time horizons, up to six months. In addition, inflation forecast generated smoothed signals, allowing to identify only the trend of inflation of up to twelve months

Crogging (cross-validation aggregation) for forecasting - A novel algorithm of neural network ensembles on time series subsamples

In classification, regression and time series prediction alike, cross-validation is widely employed to estimate the expected accuracy of a predictive algorithm by averaging predictive errors across mutually exclusive subsamples of the data. Similarly, bootstrapping aims to increase the validity of estimating the expected accuracy by repeatedly sub-sampling the data with replacement, creating overlapping samples of the data. Estimates are then used to anticipate of future risk in decision making, or to guide model selection where multiple candidates are feasible. Beyond error estimation, bootstrapping has recently been extended to combine each of the diverse models created for estimation, and aggregating over each of their predictions (rather than their errors), coined bootstrap aggregation or bagging. However, similar extensions of cross-validation to create diverse forecasting models have not been considered. In accordance with bagging, we propose to combine the benefits of cross-validation and forecast aggregation, i.e. crogging. We assesses different levels of cross-validation, including a (single-fold) hold-out approach, 2-fold and 10-fold cross validation and Monte-Carlos cross validation, to create diverse base-models of neural networks for time series prediction trained on different data subsets, and average their individual multiple-step ahead predictions. Results of forecasting the 111 time series of the NN3 competition indicate significant improvements accuracy through Crogging relative to Bagging or individual model selection of neural networks