Short-term solar radiation forecasting by using an iterative combination of wavelet artificial neural networks

The information provided by accurate forecasts of solar energy time series are considered essential for performing an appropriate prediction of the electrical power that will be available in an electric system, as pointed out in Zhou et al. (2011). However, since the underlying data are highly non-stationary, it follows that to produce their accurate predictions is a very difficult assignment. In order to accomplish it, this paper proposes an iterative Combination of Wavelet Artificial Neural Networks (CWANN) which is aimed to produce short-term solar radiation time series forecasting. Basically, the CWANN method can be split into three stages: at first one, a decomposition of level p, defined in terms of a wavelet basis, of a given solar radiation time series is performed, generating r+1 Wavelet Components (WC); at second one, these r+1 WCs are individually modeled by the k different ANNs, where k>5, and the 5 best forecasts of each WC are combined by means of another ANN, producing the combined forecasts of WC; and, at third one, the combined forecasts WC are simply added, generating the forecasts of the underlying solar radiation data. An iterative algorithm is proposed for iteratively searching for the optimal values for the CWANN parameters, as we will see. In order to evaluate it, ten real solar radiation time series of Brazilian system were modeled here. In all statistical results, the CWANN method has achieved remarkable greater forecasting performances when compared with a traditional ANN (described in Section 2.1)

COMBINAÇÃO LINEAR WAVELET SARIMA-RNA COM ESTÁGIOS MULTIPLOS NA PREVISÃO DE SÉRIES TEMPORAIS

In this paper, we put forward a hybrid methodology for combining forecasts to (stochastic) time series referred to as Wavelet Linear Combination (WLC) SARIMA-RNA with Multiple Stages. Firstly, the wavelet decomposition of level p is performed, generating (approximations of the) p+1 wavelet components (WCs). Then, the WCs are individually modeled by means of a Box and Jenkins’ model and an artificial neural network - in order to capture, respectively, plausible linear and non-linear structures of autodependence - for, then, being linearly combined, providing hybrid forecasts for each one. Finally, all of them are linearly combined by the WLC of forecasts (to be defined). For evaluating it, we used the Box and Jenkins’ (BJ) models, artificial neural networks (ANN), and its traditional Linear Combination (LC1) of forecasts; and ANN integrated with the wavelet decomposition (ANNWAVELET), BJ model integrated with the wavelet decomposition (BJ-WAVELET), and its conventional Linear Combination (LC2) of forecasts. All predictive methods applied to the monthly time series of average flow of tributaries of the Itaipu Dam dam, located in Foz do Iguaçu, Brazil. In all analysis, the proposed hybrid methodology has provided higher predictive performance than the other ones

Previsão espacial de demanda em sistemas de distribuição com uma base reduzida de dados

A previsão espacial de demanda em sistemas de distribuição de energia elétrica visa determinar a distribuição espaço-temporal do crescimento da demanda de energia elétrica na área de serviço. Essa informação é de vital importância para apoiar o processo de tomada de decisões no planejamento da expansão e operação das redes de distribuição no médio e longo prazo. As metodologias de previsão espacial de demanda requerem uma grande quantidade de dados do sistema elétrico, das características socioeconômicas da região e da população, que geralmente não são de fácil coleta nem manipulação, e muitas vezes não estão disponíveis. Nesta tese apresenta-se e aplica-se uma nova metodologia de previsão espacial de demanda, a partir de um algoritmo de extração de conhecimento baseado em conceitos de algoritmos evolutivos e regras de classificação lingüísticas, para caracterizar a área de serviço e identificar novas áreas com possibilidades de aumento de carga futuro. Com o algoritmo desenvolvido, toda a informação disponível de uma base de dados espacial é extraída, sem importar o tamanho desta, assim, apresenta como flexibilidade o fato de poder ser aplicado em diferentes situações. Além disso, permite o acesso a novas e diferentes bases de dados no futuro. A metodologia proposta foi aplicada em um sistema real de uma cidade de porte médio, com cerca de duzentos mil habitantes, apresentado respostas com acerto em torno de 95%, quando comparadas com as obtidas por especialistas que realizam a projeção de demanda na região. Adicionalmente, são obtidos resultados importantes sobre áreas do município com potencial de desenvolvimento, no longo prazo, que geralmente não são indicadas pelos especialistas.Spatial electric load forecasting in electric energy distribution systems try to find out the spatial-temporal distribution of the electric energy demand growth in the service area. This information is of vital importance to support the decision-making process in planning the expansion and operation of distribution networks in the medium and long term. The methodologies for spatial electric load forecasting require many data from the electrical system, socioeconomic characteristics of the region and population, which generally are not easy to collect or manipulate, and often are not available. In this thesis, a new spatial electric load forecasting methodology is presented. To characterize the service area and identify the new areas with growth possibility in the future, the methodology uses elements from knowledge extraction, language based classification rules and evolutionary algorithms. The algorithm developed extract all available information from a spatial database no matter the size of it, thus, one of its main advantages is the flexibility to be applied in different situations. In addition, it allows access to new and different databases in the future. The proposed methodology was applied in a real distribution system of a medium size city, about two hundred thousand inhabitants, with a 95% success rate when compared with results obtained by specialists who perform the load forecasting in the region. Also, important results are obtained about areas of the town with development potential in the long term, which generally are not listed by the specialists.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Distribución de costos en el planeamiento de sistemas de transmisión de gran tamaño usando teoría de juegos

En las actuales estructuras de mercados eléctricos descentralizados, los sistemas de transmisión continúan siendo monopolio natural para garantizar la seguridad del sistema y el libre acceso para todos los agentes, así, la expansión del sistema debe ser coordinada de tal manera que favorezca por igual a todos los agentes. Este trabajo presenta una metodología de distribución de costos de inversión para la expansión de los sistemas de transmisión, entre los agentes del sistema, utilizando conceptos de teoría de juegos, para que los pagos sean “justos”. El método es aplicado a un sistema basado en el sistema Colombiano presentando buenos resultados.In the actual decentralized electrical system structures, the transmission systemsn continue to be a natural monopoly in order to guarantee the system security and an open access to all the agents, thus, the system expansion must be coordinated so all the agents receive the benefits. This work presents a cost allocation methodology for the expansion of transmission systems, among the system agents, using game theory concepts, so the share for each one is considered “fair”. The method is tested in a system based on the Colombian System presenting good results