Evaluation of the economic viability of grid-connected photovoltaic systems in different Brazilian regions

This study aimed at the dimensioning and economic analysis of grid-connected photovoltaic systems in different cities in Brazil. As a criterion for the selection of the cities of interest, it was considered the capital with the highest number of residences in each of the five regions of the country, as well as the city where the authors are established, namely the cities of Salvador, Manaus, Goiânia, São Paulo, Curitiba, and Cascavel. Based on local characteristics and adopting an average monthly electricity consumption of 400 kWh, was developed the dimensioning of the photovoltaic systems, adopting the methodology presented by Pinho and Galdino (2014). Based on the components determined for the systems, budgets were made to enable their implementation. The investments to be made varied between US$ 4,682.97 and US$ 5,326.06. From these values, economic analyses were made using the discounted payback method. The projects presented different payback times, with the shortest payback time to the city of Manaus, with 9 years and the longest in the city of Curitiba, with 15 years. Therefore, it was confirmed the research hypothesis that the regional characteristics linked to the dimensioning of the photovoltaic systems directly affect the time of return on investment.

A Novel Decomposition and Combination Technique for Forecasting Monthly Electricity Consumption

With the share of electricity in total final energy consumption increasing quickly, the world is becoming increasingly dependent on electricity, which makes it more and more important to improve the forecasting accuracy of electricity consumption to ensure the normal operation of economic activities. In this paper, a novel decomposition and combination technique to forecast monthly electricity consumption is proposed. First, we use STL decomposition to obtain the trend, season, and residual components of the time series. Second, we use SARIMA, SVR, ANN, and LSTM to forecast trend, season, and residual component, respectively. Third, we use time correlation principle to improve the forecasting accuracy of season component. Fourth, we integrated the residual component predicted by SARIMA, SVR, ANN, and LSTM into a new sequence to improve the forecasting accuracy of residual component. In order to verify the performance of the proposed forecast model, monthly electricity consumption data in China is introduced as an example for empirical analysis. The results show that after STL decomposition, time correlation modification, and residual modification, the forecasting accuracy of each model has been gradually improved. We believe that the proposed forecast model in this paper can also be used to solve other mid- and long-term forecasting problems with obvious seasonal characteristics.