Electricity price forecasting plays a vital role in the financial markets. This paper proposes a self-adaptive, decomposed, heterogeneous, and ensemble learning model for short-term electricity price forecasting one, two, and three-months-ahead in the Brazilian market. Exogenous variables, such as supply, lagged prices and demand are considered as inputs signals of the forecasting model. Firstly, the coyote optimization algorithm is adopted to tune the hyperparameters of complementary ensemble empirical mode decomposition in the pre-processing phase. Next, three machine learning models, including extreme learning machine, gradient boosting machine, and support vector regression models, as well as Gaussian process, are designed with the intent of handling the components obtained through the signal decomposition approach with focus on time series forecasting. The individual forecasting models are directly integrated in order to obtain the final forecasting prices one to three-months-ahead. In this case, a grid of forecasting models is obtained. The best forecasting model is the one that has better generalization out-of-sample. The empirical results show the efficiency of the proposed model. Additionally, it can achieve forecasting errors lower than 4.2% in terms of symmetric mean absolute percentage error. The ranking of importance of the variables, from the smallest to the largest is, lagged prices, demand, and supply. This paper provided useful insights for multi-step-ahead forecasting in the electrical market, once the proposed model can enhance forecasting accuracy and stability.

This paper aims to present a systematic for the analysis of the economic viability of investment projects (SAEVIP) in fixed assets. In the literature, it is possible to identify the fundamental elements to evaluate the merit of the investment project (IP). To assess the dimension 'return', indicators are analysed: net present value (NPV), NPV annualised (NPVA), index benefit/cost (IBC), return on investment annualised (ROIA), index ROIA/minimum rate of attractiveness (MRA) and return on investment (ROI). An indicator analysis for dimension 'risk' is performed by internal rate of return (IRR), Payback, index MRA/IRR, index Payback/N and Fisher point. In addition, a joint assessment of indicator of risk and return is performed. A sensitivity study is promoted on the main variables involved in economic performance of the IP (MRA, costs and revenues). To validate the SAEVIP proposal, a case study shows the wealth of information generated by the application of this systematic.Keywords: decision-making; IP; investment projects; fixed assets; economic analysis; sensitivity analysis; costs and revenues; discounted cash flow; multi-index methodology; returns and risks; verticalisation of production; planning and product development.

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The ideal configuration of wastewater treatment system (WTS) for attending cities specificities has become a complex decision, due to the fact that there are several available technologies, and a diversity of characteristics presented in the scenario of each city. Considering the importance of economic analysis, especially in developing countries, this work aims to demonstrate the economic feasibility considering cost-related indicators for the ideal WTS selection for specific features in these cities. Based on a literature review, 37 main WTS and two economic cost-related indicators (Net Present Value and Annualized Net Present Value) were considered. First of all, using a multi-criteria analysis these WTS were grouped in classes using the ELECTRE TRI method, based on criteria related to efficiency, and the weights were defined by appointments from research specialists in the literature appointments. The economic analysis was performed using the Monte Carlo Simulation (MCS) method, which has been applied specifically to each WTS class, thus generating a framework of economic viability for this context. The WTS with low and high costs were appointed, considering the development level in each applied scenario. This work contributes to expanding the WTS study horizons to select an ideal system, considering the economic aspect.

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