A Fuzzy Approach to Assess the Perception of a Rural Community Concerning the Impact of Distributed Power Generation on Local Sustainability

Abstract This paper aims to evaluate the perception of residents of a rural quilombola community, about the impacts of distributed energy generation (DG) on the social, economic and environmental dimensions. The main challenge of the proposed model was to quantify the main perceptions of the target population of the research, as well as maintain the coherence of the specifications of the sustainability parameters. Diffuse modeling allows the transformation of linguistic variables into numerical values, the disadvantage is the dependence of the specialist to construct the rules. The methodology used was the application of a semi-structured questionnaire, and the results were used as a reference to build the discourse domain of the input variables of the Fuzzy inference system, which generated an index of 54.1% classified in the category partially sustainable. Specifically, the economic and social dimensions obtained an index of 46.7% and the environmental dimension of 69%. From the perspective of the perception of the respondents, the variables with the greatest impact were: landscape change (LCH) 92%, environmental awareness (EA) and reduction of global warming (GW) with values of both 69%. The variable of the most prominent economic dimension was: cost of the system with a value of 69%. In the social dimension, the variables with the greatest impact were: Community Acceptability (AC), Expansion of the support network (ESN) with values of 69%. The proposed model allowed us to interpret the respondents' perception, and can be used to generate effective actions that solve the identified demands.

Forecasting of the unknown end-of-life tire flow for control and decision making in urban solid waste management: A case study.

Efficient urban planning requires managers' experience and knowledge of reverse logistics in solid urban waste processes. Forecasting tools are needed to control, select and manage municipal solid waste. This paper presents the application of dynamic modeling approaches, namely, a linear autoregressive seasonal model, a model based on a FeedForward Artificial Neural Network and a Recurrent Neural Networks model, in order to forecast the unknown flows of end-of-life tires 12 months ahead. The models were identified using a database comprising four years of historical series related to the unknown flows of end-of-life tires. These were obtained through an exploratory analysis based on the annual sales reports of new tires issued by the Brazilian Institute of Geography and Statistics and reports related to the number of vehicles in circulation issued by Brazil's National Traffic Department. The results show that the models are able to carry out consistent forecasts over the horizon of a year ahead and the predictions are capable of identifying seasonalities and supporting decision making in urban waste management.