José Francisco Moreira Pessanha

HYDROELECTRIC ENERGY FORECAST

The aim of this paper is to propose a new methodology for hydroelectric energy forecasting. A new approach for selection of the number of eigenvalues in SSA is also proposed. In this paper it is proposed the hierarchical clustering associated to PCA and integrated to ARIMA models. The proposed approach is applied to forecast the affluent flow in a hydroelectric plant located at Parana River Basin, Brazil. As a matter of fact, modeling such series is quite important for the optimal dispatch of the energy generation in Brazil due to the heavy participation of hydro plants in the country (over 85% of the generated energy comes from hydro plants).

RESIDENTIAL ELECTRICITY CONSUMPTION FORECASTING USING A GEOMETRIC COMBINATION APPROACH

The forecasting of electricity consumption and demand plays a pivotal role in electric power systems planning. This paper proposes the combination of forecasts from two approaches with the aim of improving the forecasting accuracy, in order to make the best use of the installed transmission and generating capacity. In the first approach, the consumption time series is decomposed by wavelet analysis and a Box-Jenkins model is fitted to each wavelet component, following which the individual components forecasts are added to compute the total consumption forecast. The alternative approach, uses the Singular Spectrum Analysis technique to model the consumption time series in order to shrink the noise level. Thereafter, the Box-Jenkins model is used to forecast the filtered time series, producing a second forecast for the consumption series. Eventually, the two forecasts are combined geometrically in order to minimize the mean square error. The proposed methodology is illustrated by a computational experiment with the time series of residential consumption of electricity in Brazil.

ARTIFICIAL NEURAL NETWORK AND WAVELET DECOMPOSITION IN THE FORECAST OF GLOBAL HORIZONTAL SOLAR RADIATION

This paper proposes a method (denoted by WD-ANN) that combines the Artificial Neural Networks (ANN) and the Wavelet Decomposition (WD) to generate short-term global horizontal solar ra- diation forecasting, which is an essential information for evaluating the electrical power generated from the conversion of solar energy into electrical energy. The WD-ANN method consists of two basic steps: firstly, it is performed the decomposition of level p of the time series of interest, generating p + 1 wavelet orthonormal components; secondly, the p + 1 wavelet orthonormal components (generated in the step 1) are inserted simultaneously into an ANN in order to generate short-term forecasting. The results showed that the proposed method (WD-ANN) improved substantially the performance over the (traditional) ANN method.

Combining singular spectrum analysis and PAR(p) structures to model wind speed time series

Singular spectrum analysis (SSA) is a technique that decomposes a time series into a set of components, such as, trend, harmonics, and residuals. Leaving out the residual components and adding up the others, the time series can be smoothed. This procedure has been used to model Brazilian electricity consumption and flow series. The PAR(p), periodic autoregressive models, has been broadly used in modelling energy series in Brazil. This paper presents an approach of this decomposition method, by fitting the PAR(p), considering its multivariate version known as multivariate SSA (MSSA). The method was applied to a vector of two wind speed series recorded at two locations in the Brazilian Northeast region. The obtained results, when compared to the univariate decomposition of each series, were far superior, showing that the spatial correlation between the two series were considered by MSSA decomposition stage.

Custos operacionais eficientes das distribuidoras de energia elétrica: um estudo comparativo dos modelos DEA e SFA

This paper shows the efficiency measurements of 40 Brazilian electricity distribution companies. The efficiency scores are obtained using the data envelopment analysis (DEA) and stochastic frontier analysis (SFA) models, techniques that can reduce the information asymmetry and improve the regulator skills to compare the performance of the electricity companies, which are fundamental aspects of regulatory regimes. The two approaches are described, and the main results obtained from the different models are compared.

Avaliação dos custos operacionais eficientes das empresas de transmissão do setor elétrico Brasileiro: uma proposta de adaptação do modelo dea adotado pela ANEEL

In the Brazilian power sector, the transmission companies (TRANSCOS) receive revenues by the availability of their transmission facilities, regardless of the amount of the electric power transmitted. To promote their efficient operation the regulator periodically revises the revenue caps. The calculation of efficient operational costs is the first stage of the tariff revision process. Recently, the regulator agent published a resolution describing the methodology used to set the revenues of the Brazilian TRANSCOS. This algorithm includes a data envelopment model (DEA) which is described in this paper. In this work we propose an alternative DEA model and present a comparison of the results obtained by the two models.

Um modelo de análise envoltória de dados para o estabelecimento de metas de continuidade do fornecimento de energia elétrica

The main dimension of the electricity quality is the supply continuity. It is evaluated by indices SAIDI (System Average Interruption Duration Index) and SAIFI (System Average Interruption Frequency Index). This paper presents a new implementation of the yardstick competition that combines two Data Envelopment Analysis models (DEA) to set the continuity standards for the electricity distribution utilities and their groups of consumption units. The approach has two stages. First, a classical DEA model performs a comparative analysis between utilities to define global continuity standards for each utility; next, based on global standards, a model for resource allocation based on DEA establishes the local continuity standards for groups of consumption units in the same utility. Local standards for the consumption units groups of the main distribution utilities in the Rio de Janeiro State are presented.

Forecasting Long-term Electricity Demand in the Residential Sector☆

Abstract This work describes a methodology for long-term electricity demand forecast in the residential sector. The methodology has been used in the power market studies of some Brazilian distribution utilities. The methodology is based on decomposition of the total electricity residential consumption in three components: average consumption per consumer unit, electrification rate and number of households. Then, the forecast for the total electricity consumption in residential sector is the product of forecasts for these three components. The prediction for the number of households is based on demographic models while the future trajectory of the electrification rate is defined by the targets for achieving the universal access to electricity. The product of these two components provides a forecast to the number of residential customers. The average consumption per unit consumer depends on the macroeconomic scenarios for GDP, average household income and income distribution. The proposed methodology provides a framework to integrate macroeconomic scenario, demographic projection and assumptions for ownership and efficiency of electric appliances in a long-term demand forecast. In order to illustrate the application of the proposed methodology, this paper presents a ten-year demand forecasts for the residential sector in Brazil.

Introducing DBSCAN in the singular spectrum analysis procedure

This work proposes using DBSCAN to recognition of noise components of eigentriples in the grouping stage of SSA. The DBSCAN is a modern (revised in 2013) and expert method at to identify noise through regions of lower density. The hierarchical clustering method was the last innovation in noise recognition in SSA approach, implemented on package RSSA. However, it repeated in the literature that the hierarquical clustering method is very sensitive to noise, is unable to separate it correctly, and should not be used in clusters with varying densities and neither works well in clustering time series of different trends. Unlike, the density based clustering methods are effective in separating the noise from the data and dedicated to work well on data from different densities. This work shows better efficiency of DBSCAN over the others methods already used in this stage of SSA, because it allows considerable reduction of noise and provides better forecasting. The result is supported by experimental evaluations realized for simulated stationary and non-stationary series. The proposed combination of methodologies also was applied successfully to forecasting a real series of winds speed.

Avaliação cruzada das distribuidoras de energia elétrica

In its third round of tariff review, the Brazilian Electricity Regulatory Agency (Aneel) adopted a methodology based on data envelopment analysis (DEA) to define efficient levels of operational expenditure. This work presents proposals to improve Aneels methodology. In particular, we propose the segmentation of the set of distribution utilities using cluster analysis techniques to establish fair comparisons between utilities. Additionally, we propose a cross evaluation model using the definition of efficient operational expenditure levels to achieve an efficiency index that accounts for peer evaluations.