Lucio Ciabattoni

Fuzzy logic based economical analysis of photovoltaic energy management

Since 2002 the European Union has seen a rapid growth in the photovoltaic (PV) sector. During the last two years incentives for PV installations were cut almost worldwide slowing the growth of the market. In this scenario the design of a new plant ensuring economic convenience is strongly related to household electricity consumption patterns and energy management actions. This paper presents a high-resolution model of domestic electricity use based on Fuzzy Logic Inference System. Taking into account consumers sensibility concerning the rational use of energy, the model gives as output a 1-min resolution overall electricity usage pattern of the household. The focus of this work is the use of a novel fuzzy model combined with a cost benefits analysis to evaluate the real economic benefits of load shifting actions. A case study is presented to quantify its effectiveness in the new net metering Italian scenario.

Smartphone based Fuzzy Logic freezing of gait detection in Parkinson's Disease

The freezing of gait (FOG) is a common and highly distressing motor symptom of patients with Parkinsons Disease (PD). Effective management of FOG is difficult given its episodic nature, heterogeneous manifestation and limited responsiveness to drug treatment. Clinicians found alternative approaches, such as rhythmic cueing. We built a smartphone-based architecture in agreement with acceptability and usability requirements which is able to gather data and information useful to detect FOG. In this work fusing together the information of freeze index, energy, cadency variation and the ratio of the derivative of the energy a novel Fuzzy Logic based algorithm is developed. Performances of the Fuzzy algorithm are compared with two other algorithms showing its capability to reduce false negative detection thus improving sensitivity and specificity.

Online tuned neural networks for PV plant production forecasting

The paper deals with the forecast of the power production for three different PhotoVoltaic (PV) plants using an on-line self learning prediction algorithm. The plants are located in Italy at different latitudes. This learning algorithm is based on a radial basis function (RBF) network and combines the growing criterion and the pruning strategy of the minimal resource allocating network technique. Its on-line learning mechanism gives the chance to avoid the initial training of the NN with a large data set. The performances of the algorithm are tested on the three PV plants with different peak power, panels materials, orientation and tilting angle. Results are compared to a classical RBF neural network.

A Fuzzy Logic tool for household electrical consumption modeling

This paper presents a high-resolution model of domestic electricity use, based on Fuzzy Logic Inference System (FIS). The model is built with a “bottom-up” approach and the basic block is the single appliance. Using as inputs patterns of active occupancy (i.e. when people are at home and awake) and typical domestic habits (i.e. start frequency of some appliances), the FIS model give as output the starting probability of each appliance. A post processor enable the appliances start in order to create a one-min resolution electricity demand data. In order to validate the model, electricity demand was recorded over the period of one year within 12 dwellings in the central east coast of Italy. A thorough quantitative comparison is made between the synthetic and measured data sets, showing them to have similar statistical characteristics.

A smart lighting system for industrial and domestic use

The goal of this work is to develop a smart LED lighting system for industrial and domestic use, taking into account visual comfort and energy saving of interior lighting. The idea is to control the lighting level in an energy efficient way, keeping a desired light level where it is needed, while regulating it to a minimum where not required. In order to achieve this goal, a single control unit is needed for each lamp. In this way the system can individually control the desired light level, adapting the LED illumination according to the environment in which it is installed, by means of light sensors, motion sensors and a smart control system. Experimental results are provided to show the effectiveness of the proposed solution.

Supervisory control of PV-battery systems by online tuned neural networks

The paper deals with a neural network based supervisor control system for a PhotoVoltaic (PV) plant. The aim of the work is to feed the power line with the 24 hours ahead forecast of the PV production. An on-line self-learning prediction algorithm is used to forecast the power production of the PV plant. The learning algorithm is based on a Radial Basis Function (RBF) network and combines the growing criterion and the pruning strategy of the minimal resource allocating network technique. The power feeding the electric line is scheduled by a Fuzzy Logic Supervisor (FLS) which controls the charge and discharge of a battery used as an energy buffer. The proposed solution has been experimentally tested on a 14 KWp PV plant and a lithium battery pack.

Design of a Home Energy Management System by Online Neural Networks

Abstract In this paper the design and test of a home energy management system have been considered. The device, monitoring home loads, detecting and forecasting photovoltaic (PV) power production and home consumptions, informs and influence users behaviour on their energy demand. A neural network based self-learning prediction algorithm is used to forecast the power production of the PV plant and the household consumptions over a determined time horizon. A semi auto active demand side management technique is used to maximize the amount of PV electricity directly used on-site. The proposed solution has been experimentally tested in 3 houses with 3.3 KWp PV plant.

On line solar irradiation forecasting by minimal resource allocating networks

The paper describes an on-line prediction algorithm to estimate, over a determined time horizon, the solar irradiation of a specific site. The learning algorithm is based on a radial basis function network and combines the growing criterion and the pruning strategy of the minimal resource allocating network technique. An Extended Kalman Filter (EKF) is used to update all the parameters of the network. The on-line algorithm is able to avoid the initial training of the neural network. A comparison of the performance obtained by the MRAN EKF RBF Neural Network with respect to the standard RBF Neural Network is presented.

Online tuned neural networks for fuzzy supervisory control of pv-battery systems

The paper deals with a neural network based fuzzy supervisor control to manage power flows in a Photo-Voltaic (PV) - Battery system. An on-line self-learning prediction algorithm is used to forecast, over a determined time horizon, the power mismatch between PV production and electrical consumptions. The learning algorithm is based on a Radial Basis Function (RBF) network and combines the growing criterion and the pruning strategy of the minimal resource allocating network technique. The power flows are scheduled by a Fuzzy Logic Supervisor (FLS) which controls the charge and discharge of a battery used as an energy buffer. The proposed solution has been experimentally tested on a 14 KWp PV plant and a lithium battery pack.

Multi-apartment residential microgrid monitoring system based on kernel canonical variate analysis

In the residential energy sector there is a growing interest in smart energy management systems able to monitor, manage and minimize energy consumption. A key factor to curb household energy consumption is the amendment of occupant erroneous behaviors and systems malfunctioning. In this scenario energy efficiency benefits can be either amplified or neutralized by, respectively, good or bad practices carried out by end users. Authors propose a diagnostic system for a residential microgrid application able to detect faults and occupant bad behaviors. In particular a nonlinear monitoring method, based on kernel canonical variate analysis, is developed. To overcome the normality assumption regarding the signals probability distribution, Upper Control Limits are derived from the estimated Probability Density Function through Kernel Density Estimation. The proposed method, applied to a smart residential microgrid, is tested on experimental data acquired from July 2012 to October 2013.