Prediction in Photovoltaic Power by Neural Networks

The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels. In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference system, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches

Linearity Testing Against a Fuzzy Rule-based Model

In this paper, we introduce a linearity test for fuzzy rule-based models in the framework of time series modeling. To do so, we explore a family of statistical models, the regime switching autoregressive models, and the relations that link them to the fuzzy rule-based models. From these relations, we derive a Lagrange Multiplier linearity test and some properties of the maximum likelihood estimator needed for it. Finally, an empirical study of the goodness of the test is presented.fuzzy rule-based models, time series, linearity test, statistical inference

The commodity chain of the household: from survey design to policy and practice

Data collection and analysis and policy formulation all require a social unit to be defined, generally called the household. Multidisciplinary evidence shows that households as defined by survey practitioners often bear little resemblance to lived socio-economic units. This study examines how a shared language, the 'household', can generate misunderstandings because different groups with distinctive understandings of the term 'household' are often unaware that others may be using ‘household’ differently. Results from 4 interlinked and iterative methods are presented: review of household survey documentation (1950s-present); ethnographic ground-truthing fieldwork; in-depth key informant interviews; and modelling. Results show that whereas data collectors have a clear idea of what a `household` is, data users are often unaware of the nuances of the constraints imposed by data collection. This has implications for policy planning and practice. What interviewees consider when they think of their household can differ systematically from data collectors' definitions

Design of an Adaptive Neurofuzzy Inference Control System for the Unified Power-Flow Controller

This paper presents a new approach to control the operation of the unified power-flow controller (UPFC) based on the adaptive neurofuzzy inference controller (ANFIC) concept. The training data for the controller are extracted from an analytical model of the transmission system incorporating a UPFC. The operating points' space is dynamically partitioned into two regions: 1) an inner region where the desired operating point can be achieved without violating any of the UPFC constraints and 2) an outer region where it is necessary to operate the UPFC beyond its limits. The controller is designed to achieve the most appropriate operating point based on the real power priority. In this study, the authors investigated and analyzed the effect of the system short-circuit level on the UPFC operating feasible region which defines the limitation of its parameters. In order to illustrate the effectiveness of the control algorithm, simulation and experimental studies have been conducted using the MATLAB/SIMULINK and dSPACE DS1103 data-acquisition board. The obtained results show a clear agreement between simulation and experimental results which verify the effective performance of the ANFIC controller