Antonello Rosato

Embedding of time series for the prediction in photovoltaic power plants

The ability to forecast the power produced by renewable energy plants in short and middle terms is a key issue to allow an high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the Trasmission System Operator level, as well as electrical distributors and power system operators level. In this paper we present three techniques based on neural and fuzzy neural networks, 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.

Distributed learning of Random Weights Fuzzy Neural Networks

In this paper, we propose a scalable, decentralized learning algorithm for Random Weights Fuzzy Neural Networks, when training data is distributed through a network of interconnected computing agents. In this scenario, the aim is for all the agents to converge to a single model, with the requirement that only local communications between the agents are permitted. In this work we assume that all the agents know the parameters of the antecedents, while the parameters of the consequents are estimated by using the Alternating Direction Method of Multipliers strategy. Experimental results show that the performance of the proposed algorithm is comparable to that of a centralized model, where all the data is collected by a single agent before the training process. To this date, this is the first publication that addressed the problem of training a fuzzy neural network over a fully decentralized infrastructure.

Recent Advances on Distributed Unsupervised Learning

Distributed machine learning is a problem of inferring a desired relation when the training data is distributed throughout a network of agents (e.g. sensor networks, robot swarms, etc.). A typical problem of unsupervised learning is clustering, that is grouping patterns based on some similarity/dissimilarity measures. Provided they are highly scalable, fault-tolerant and energy efficient, clustering algorithms can be adopted in large-scale distributed systems. This work surveys the state-of-the-art in this field, presenting algorithms that solve the distributed clustering problem efficiently, with particular attention to the computation and clustering criteria.

Finite precision implementation of random vector functional-link networks

The increasing amount of data to be processed coming from multiple sources, as in the case of sensor networks, and the need to cope with constraints of security and privacy, make necessary the use of computationally efficient techniques on simple and cheap hardware architectures often distributed in pervasive scenarios. Random Vector Functional-Link is a neural network model usually adopted for processing distributed big data, but no constraints have been considered so far to deal with limited hardware resources. This paper is focused on implementing a modified version of the Random Vector Functional-Link network with finite precision arithmetic, in order to make it suited to hardware architectures even based on a simple microcontroller. A genetic optimization is also proposed to ensure that the overall performance is comparable with standard software implementations. The numerical results prove the efficacy of the proposed approach.

A nonuniform quantizer for hardware implementation of neural networks

New trends in neural computation, now dealing with distributed learning on pervasive sensor networks and multiple sources of big data, make necessary the use of computationally efficient techniques to be implemented on simple and cheap hardware architectures. In this paper, a nonuniform quantization at the input layer of neural networks is introduced, in order to optimize their implementation on hardware architectures based on a finite precision arithmetic. Namely, we propose a nonlinear A/D conversion of input signals by considering the actual structure of data to be processed. Random Vector Functional-Link is considered as the reference model for neural networks and a genetic optimization is adopted for determining the quantization levels to be found. The proposed approach is assessed by several experimental results obtained on well-known benchmarks for the general problem of data regression.

Takagi-sugeno fuzzy systems applied to voltage prediction of photovoltaic plants

High penetration level of intermittent and variable renewable electricity generation introduces signicant challenges to energy management of modern smart grids. Solar photo-voltaics and wind energy have uncertain and non-dispatchable output which leads to concerns regarding the technical and economic feasibility of a reliable integration of large amounts of variable generation into electric grids. In this scenario, accurate forecasting of renewable generation outputs is of paramount importance to secure operation of smart grids. In this paper, we present a study on the use of fuzzy neural networks and their application to the prediction of solar photovoltaic outputs. The new learning strategy is suited to any fuzzy inference model. The comparison with respect to well-known neural and fuzzy neural models will prove that our approach is able to follow the behavior of the underlying unknown process with a good prediction of the observed time series.

A new learning approach for Takagi-Sugeno fuzzy systems applied to time series prediction

In this paper, we present a study on the use of fuzzy neural networks and their application to the prediction of times series generated by complex processes of the real-world. The new learning strategy is suited to any fuzzy inference model, especially in the case of higher-order Sugeno-type fuzzy rules. The data considered herein are real-world cases concerning chaotic benchmarks as well as environmental time series. The comparison with respect to well-known neural and fuzzy neural models will prove that our approach is able to follow the behavior of the underlying, unknown process with a good prediction of the observed time series.