Massimo Grisostomi

Solid Waste Management Architecture Using Wireless Sensor Network Technology

In many application fields such as home, industry, environment and health, different Wireless Sensor Network (WSN) applications have been developed to solve management problems with smart implementations. This approach can be applied in the filed of solid waste management. In this paper a new architecture is proposed with the aim to improve the on-site handling and transfer optimization in the waste management process. The system architecture is based on sensor nodes and makes use of Data Transfer Nodes (DTN) in order to provide to a remote server the retrieved data measurements from the garbage bins filling. A remote monitoring solution has been implemented, providing user the possibility to interact with the system by using a web browser. Several activities with the aim to provide a Decision Support System (DSS) able to find solutions for resources organization problems linked to solid waste management have been started.

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.

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.

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.

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.

Neural networks based home energy management system in residential PV scenario

In this paper we propose and design a home energy management system using artificial intelligence. The device, monitoring home loads, detecting and forecasting photovoltaic (PV) power production and home consumptions, informs and influences users on their energy choices. A neural network self-learning prediction algorithm is used to forecast, over a determined time horizon, the power production of the PV plant and the consumptions of the house. The online 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. Furthermore a novel method to simulate electrical consumptions and evaluate the potential benefits of a Demand Side Management is developed. The proposed solution has been experimentally tested in 3 houses with 3.3 KWp PV plant.

Indoor thermal comfort control through fuzzy logic PMV optimization

Control and monitoring of indoor thermal conditions represent crucial tasks for peoples satisfaction in working and living spaces. Among all standards released, predicted mean vote (PMV) is the international index adopted to define users thermal comfort conditions in thermal moderate environments. PMV is a nonlinear function of various quantities, which generally limits its applicability to the heating, ventilation, and air conditioning (HVAC) control problem. Furthermore this index does not consider explicitly outdoor weather conditions. In order to overcome both problems, we introduce a novel fuzzy controller for HVAC systems. The control, considering PMV index value as well as outdoor weather conditions, has been experimentally tested in a working space in the central east coast of Italy. Furthermore temperature regulation performances have been compared with those of a classical PID.

Residential energy monitoring and management based on fuzzy logic

In the new European scenario the design of a photovoltaic plant ensuring savings on electricity bills is strongly related to energy management policies. This paper introduces a low-cost device to monitor domestic electricity consumptions, photovoltaic production and define load shifting policies based on fuzzy logic forecasts.

Application of a wireless sensor networks and Web2Py architecture for factory line production monitoring

In this paper a wireless sensors network (WSN) has been designed according to the IEEE 1451 standard and installed in a manufacturing cell. On-line monitoring data have been collected and stored in a database through a Web2Py framework. A case study on the usage of WSN to perform an industrial performance analysis is made. A set of measures, comprehensive of all the production parameters of 4 different line productions, have been used to analyze the performances of the company. Effectiveness and reliability indexes, such as the mean time to repair (MTTR) and mean time between failures (MTBF) of the machines, overall equipment effectiveness (OEE) and the total effective equipment productivity (TEEP) have been computed and compared to the world-class manufacturing (WCM) standards.