1,720,969 research outputs found
A tuning methodology of Model Predictive Control design for energy efficient building thermal control
No full textThe Model Predictive Control (MPC) approach is based on the prediction of indoor and outdoor thermal loads in order to counter the deviation of the indoor temperature from the occupants' preference in advance. The minimization of temperature alteration allows for efficient energy use and improvement of indoor thermal environment. Unlike the classical reactive control, MPC is able to act in advance and to explicitly handle constraints on building variables when selecting the best scenario. This peculiarity makes MPC particularly suitable for the optimal energy management of buildings but, despite the increasing research activity in the field, the commercial technologies are lagging behind. Among the reasons are the lack of a well established design approach easily applicable to the buildings domain and the lack of a design optimization. In this paper a tuning methodology of MPC design for energy efficient building thermal control is presented. The tuning is performed on the controller parameters, and aims at identifying the best parameter set in terms of energy saving and temperature deviation from the chosen setpoint. To demonstrate the effectiveness of the methodology, a simulation based analysis using a model estimated on a real case study is presented. The methodology shows that by improving the control parameters it is possible to reduce energy consumption and improve thermal comfort for the final user
Design and implementation of machine learning techniques for modeling and managing battery energy storage systems
Full text linkThe fast technological evolution and industrialization that have interested the humankind since the fifties has caused a progressive and exponential increase of CO2 emissions and Earth temperature. Therefore, the research community and the political authorities have recognized the need of a deep technological revolution in both the transportation and the energy distribution systems to hinder climate changes. Thus, pure and hybrid electric powertrains, smart grids, and microgrids are key technologies for achieving the expected goals. Nevertheless, the development of the above mentioned technologies require very effective and performing Battery Energy Storage Systems (BESSs), and even more effective Battery Management Systems (BMSs).
Considering the above background, this Ph.D. thesis has focused on the development of an innovative and advanced BMS that involves the use of machine learning techniques for improving the BESS effectiveness and efficiency. Great attention has been paid to the State of Charge (SoC) estimation problem, aiming at investigating solutions for achieving more accurate and reliable estimations. To this aim, the main contribution has concerned the development of accurate and flexible models of electrochemical cells.
Three main modeling requirements have been pursued for ensuring accurate SoC estimations: insight on the cell physics, nonlinear approximation capability, and flexible system identification procedures. Thus, the research activity has aimed at fulfilling these requirements by developing and investigating three different modeling approaches, namely black, white, and gray box techniques.
Extreme Learning Machines, Radial Basis Function Neural Networks, and Wavelet Neural Networks were considered among the black box models, but none of them were able to achieve satisfactory SoC estimation performances. The white box Equivalent Circuit Models (ECMs) have achieved better results, proving the benefit that the insight on the cell physics provides to the SoC estimation task. Nevertheless, it has appeared clear that the linearity of ECMs has reduced their effectiveness in the SoC task. Thus, the gray box Neural Networks Ensemble (NNE) and the white box Equivalent Neural Networks Circuit (ENNC) models have been developed aiming at exploiting
the neural networks theory in order to achieve accurate models, ensuring at the same time very flexible system identification procedures together with nonlinear approximation capabilities.
The performances of NNE and ENNC have been compelling. In particular, the white box ENNC has reached the most effective performances, achieving accurate SoC estimations, together with a simple architecture and a flexible system identification procedure.
The outcome of this thesis makes it possible the development of an interesting scenario in which a suitable cloud framework provides remote assistance to several BMSs in order to adapt the managing algorithms to the aging of BESSs, even considering different and distinct applications
A white-box equivalent neural network circuit model for SoC estimation of electrochemical cells
No full textSmart grids, microgrids, and pure electric powertrains are the key technologies for achieving the expected goals concerning the restraint of CO2 emissions and global warming. In this context, an effective use of electrochemical energy storage systems (ESSs) is mandatory. In particular, accurate state of charge (SoC) estimations are helpful for improving the ESS performances. To this aim, developing accurate models of electrochemical cells is necessary for implementing effective SoC estimators. Therefore, a novel neural network modeling technique is proposed in this paper. The main contribution consists in the development of a white-box neural design that provides helpful insights into the cell physics, together with a powerful nonlinear approximation capability, and a flexible system identification procedure. In order to do that, the system equations of a white-box equivalent circuit model (ECM) have been combined with computational intelligence techniques by approximating each circuit element with a dedicated neural network. The model performances have been analyzed in terms of model accuracy, SoC estimation effectiveness, and computational cost over two realistic data sets. Moreover, the proposed model has been compared with a white-box ECM and a gray-box neural network model. The results prove that the proposed modeling technique is able to provide useful improvements in the SoC estimation task with a competing computational cost
ALGORITMI DI INTELLIGENZA COMPUTAZIONALE PER LA MOBILITA’ SOSTENIBILE E L’EFFICIENZA ENERGETICA
No full textInstrument learning and sparse NMD for automatic polyphonic music transcription
No full textIn this paper, an Automatic Music Transcription (AMT) algorithm based on a supervised Non-negatve Matrix Decomposition (NMD) is discussed. In particular, a novel approach for enhancing the sparsity of the solution is proposed. It consists of a two-step processing in which the NMD is solved joining a `2 regularization and a threshold filtering. In the first step, the NMD is performed with the `2 regularization in order to get an overall selection of the notes most likely appearing in the monotimbral musical excerpt. In the second step, a threshold filtering followed by another `2 regularized NMD are repeatedly performed in order to progressively reduce the dictionary matrix and to refine the notes transcription. Furthermore, a useroriented instrument learning procedure has been conceived and proposed. The proposed AMT system has been tested upon the dataset collected by the LabROSA laboratories considering the transcription of three different pianos. Moreover, it has been validated through a comparison with a regularized NMD and with three open source AMT software. The results prove the effectiveness of the proposed two-step processing in enhancing the sparsity of the solution and in improving the transcription accuracy. Moreover, the proposed system shows promising performance in both multi-F0 and note tracking tasks, obtaining in most tests better transcription accuracy than the competing algorithms
A PSO algorithm for transient dynamic modeling of lithium cells through a nonlinear RC filter
No full textNowadays an effective Energy Storage System (ESS) is a fundamental requirement for any effective innovation in the fields of energetic and transportation sustainability. One of the most important device for obtaining efficient ESSs is the Battery Management System (BMS). It includes all the electronic components and algorithms for the monitoring and management of the ESS status. The key task of the BMS is the estimation of the State of Charge. Currently the most promising methods are based on state observers, which require an accurate model of the cell. In this paper a novel technique for modeling the transient behavior of the cell is proposed. It is based on a single nonlinear dipole composed of a standard linear resistor and a nonlinear voltage driven capacitor connected in parallel. A method for the parameters identification based on a Particle Swarm Optimization algorithm has been developed. Both the identification algorithm and the proposed model are validated on a A123 cell obtaining very stable solution and better accuracy with respect to models based on linear components
An ANFIS based system identification procedure for modeling electrochemical cells
No full textThe development of electrochemical cell models and of the related system identification procedures are of utmost importance for achieving effective management of electrochemical Energy Storage Systems. Specifically, accurate models are mandatory for performing effective estimation of the State of Charge (SoC) by means of Kalman Filtering approaches. Currently, some of the most promising models are those based on the equivalent circuit technique. However, these models are based on the standard definition of the SoC, which is related to the integral of the input current. The main drawback of this approach is that it is not related to any physical characteristic of the cell. A first implementation of an equivalent circuit model based on a novel mechanical inspired definition of the SoC is proposed in this paper. Herein, the cell is assimilated to a liquid tank so that the SoC, can be defined with respect to the reservoir shape. In particular, the charge reservoir has been modeled by means of an ANFIS estimator. A flexible system identification procedure has been defined by formulating a fitting problem upon generic sequences of the measured current and voltage. Specifically, a customized Particle Swarm optimization is in charge both of training the ANFIS and of identifying the other model parameters. The proposed modeling approach has been tested upon the Randomized Battery Usage Data Set and the obtained results proved its effectiveness both in emulating the cell behavior and in the SoC estimation task
A binary PSO approach for real time optimal balancing of electrochemical cells
No full textAn effective management of Electrochemical Energy Storage Systems (ESSs) is nowadays of utmost importance for the technological evolution in both automotive and sustainable power networks applications. In particular, Battery Managements Systems (BMSs) are the electronic devices devolved to this management. One of the most important task of any BMS is cells balancing, aiming at leveling the operating points of the cells composing the ESS. Therefore, a novel online balancing algorithm is proposed in this work. Differently to the most commonly used methods, the proposed approach works by leveling the State of Charge (SoC) of the cells instead of their voltages. The balancing procedure has been formulated as a zero-one integer programming to be solved online by means of a Hybrid Genetic Binary Particle Swarm optimization (BPSO). Furthermore, a sparsity regularization has been considered for improving the energetic efficiency of the algorithm. Both the baseline and the regularized balancing systems have been tested and compared with a standard voltage based approach. The results show that the proposed method achieves a better and more robust balancing of the ESS, keeping a comparable energetic efficiency with respect to the voltage based technique
A generalized framework for ANFIS synthesis procedures by clustering techniques
Full text linkThe application of machine learning and soft computing techniques for function approximation is a widely explored topic in literature. Neural networks, evolutionary algorithms and support vector machines proved to be very effective, although these models suffer from very low level of interpretability by human operators. Conversely, Adaptive Neuro Fuzzy Inference Systems (ANFISs) demonstrated to be very accurate models featured by a considerable degree of interpretability. In this paper, a general framework for ANFIS training by clustering is proposed and investigated. In particular, different derivative-free ANFIS synthesis procedures are considered for performance evaluation, by taking into account different clustering algorithms, dissimilarity measures and by including an additional neuro-fuzzy classifier downstream the clustering phase targeted to rule base refinement. The resulting ANFISs have been compared, in terms of effectiveness and efficiency, on several benchmark datasets against three suitable competitors, namely a Support Vector Regression, MultiLayer Perceptron and a K-Nearest Neighbour decision rule. Computational results show that the proposed techniques tend to outperform competing strategies while, at same time, featuring models with lower structural complexity. A complete software suite implementing the proposed framework is freely available under an open-source licence
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