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This studyaimed at improving the performance and efficiency of conventional static photovoltaic (PV) systems by introducing a metaheuristic algorithm-based approach that involves reconfiguring electrical wiring using switches under different shading profiles. Themetaheuristicalgorithmused wasthe firefly algorithm (FA), which controls the switching patterns under non-homogenous shading profiles and tracks the highest global peak of power produced by the numerous switching patterns. This study aimed to solve the current problems faced by static PV systems, such as unequal dispersion of shading affecting solar panels, multiple peaks, and hot spot phenomena, which can contribute to significant power loss and efficiency reduction. The experimental setup focusedon software development and the system or model developed in the MATLAB Simulink platform. Athorough and comprehensive analysis was done by comparing the proposed method’s overall performance and power generation with thenovel static PVseries–parallel (SP) topology and totalcross-tied (TCT) scheme. The SP configuration is widely used in the PV industry. However, the TCT configuration has superior performance and energy yield generation compared to other static PV configurations, such as the bridge-linked (BL) and honey comb (HC) configurations. The results presented in this paper provide valuable information about the proposed method’s features with regard toenhancing the overall performance and efficiency of PV arrays.
Mohammad Nazeri; Mohammad Tajuddin; Thanikanti Babu; Azralmukmin Azmi; Maria Malvoni; Nallapaneni Kumar. Firefly Algorithm-Based Photovoltaic Array Reconfiguration for Maximum Power Extraction during Mismatch Conditions. Sustainability 2021, 13, 3206 .
AMA StyleMohammad Nazeri, Mohammad Tajuddin, Thanikanti Babu, Azralmukmin Azmi, Maria Malvoni, Nallapaneni Kumar. Firefly Algorithm-Based Photovoltaic Array Reconfiguration for Maximum Power Extraction during Mismatch Conditions. Sustainability. 2021; 13 (6):3206.
Chicago/Turabian StyleMohammad Nazeri; Mohammad Tajuddin; Thanikanti Babu; Azralmukmin Azmi; Maria Malvoni; Nallapaneni Kumar. 2021. "Firefly Algorithm-Based Photovoltaic Array Reconfiguration for Maximum Power Extraction during Mismatch Conditions." Sustainability 13, no. 6: 3206.
Harnessing energy from the sunlight using solar photovoltaic trees (SPVTs) has become popular at present as they reduce land footprint and offer numerous complimentary services that offset infrastructure. The SPVT’s complimentary services are noticeable in many ways, e.g., electric vehicle charging stations, landscaping, passenger shelters, onsite energy generated security poles, etc. Although the SPVT offers numerous benefits and services, its deployment is relatively slower due to the challenges it suffers. The most difficult challenges include the structure design, the photovoltaic (PV) cell technology selection for a leaf, and uncertainty in performance due to weather parameter variations. This paper aims to provide the most practical solution supported by the performance prioritization approach (PPA) framework for a typical multilayered SPVT. The proposed PPA framework considers the energy and sustainability indicators and helps in reporting the performance of a multilayered SPVT, with the aim of selecting an efficient PV leaf design. A three-layered SPVT (3-L SPVT) is simulated; moreover, the degradation-influenced lifetime energy performance and carbon dioxide (CO2) emissions were evaluated for three different PV-cell technologies, namely crystalline silicon (c-Si), copper indium gallium selenide (CIGS), and cadmium telluride (CdTe). While evaluating the performance of the 3-L SPVT, the power conversion efficiency, thermal regulation, degradation rate, and lifecycle carbon emissions were considered. The results of the 3-L SPVT were analyzed thoroughly, and it was found that in the early years, the c-Si PV leaves give better energy yields. However, when degradation and other influencing weather parameters were considered over its lifetime, the SPVT with c-Si leaves showed a lowered energy yield. Overall, the lifetime energy and CO2 emission results indicate that the CdTe PV leaf outperforms due to its lower degradation rate compared to c-Si and CIGS. On the other side, the benefits associated with CdTe cells, such as flexible and ultrathin glass structure as well as low-cost manufacturing, make them the best acceptable PV leaf for SPVT design. Through this investigation, we present the selection of suitable solar cell technology for a PV leaf.
Nallapaneni Manoj Kumar; Shauhrat S. Chopra; Maria Malvoni; Rajvikram Madurai Elavarasan; Narottam Das. Solar Cell Technology Selection for a PV Leaf Based on Energy and Sustainability Indicators—A Case of a Multilayered Solar Photovoltaic Tree. Energies 2020, 13, 6439 .
AMA StyleNallapaneni Manoj Kumar, Shauhrat S. Chopra, Maria Malvoni, Rajvikram Madurai Elavarasan, Narottam Das. Solar Cell Technology Selection for a PV Leaf Based on Energy and Sustainability Indicators—A Case of a Multilayered Solar Photovoltaic Tree. Energies. 2020; 13 (23):6439.
Chicago/Turabian StyleNallapaneni Manoj Kumar; Shauhrat S. Chopra; Maria Malvoni; Rajvikram Madurai Elavarasan; Narottam Das. 2020. "Solar Cell Technology Selection for a PV Leaf Based on Energy and Sustainability Indicators—A Case of a Multilayered Solar Photovoltaic Tree." Energies 13, no. 23: 6439.
Smart grid (SG), an evolving concept in the modern power infrastructure, enables the two-way flow of electricity and data between the peers within the electricity system networks (ESN) and its clusters. The self-healing capabilities of SG allow the peers to become active partakers in ESN. In general, the SG is intended to replace the fossil fuel-rich conventional grid with the distributed energy resources (DER) and pools numerous existing and emerging know-hows like information and digital communications technologies together to manage countless operations. With this, the SG will able to “detect, react, and pro-act” to changes in usage and address multiple issues, thereby ensuring timely grid operations. However, the “detect, react, and pro-act” features in DER-based SG can only be accomplished at the fullest level with the use of technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and the Blockchain (BC). The techniques associated with AI include fuzzy logic, knowledge-based systems, and neural networks. They have brought advances in controlling DER-based SG. The IoT and BC have also enabled various services like data sensing, data storage, secured, transparent, and traceable digital transactions among ESN peers and its clusters. These promising technologies have gone through fast technological evolution in the past decade, and their applications have increased rapidly in ESN. Hence, this study discusses the SG and applications of AI, IoT, and BC. First, a comprehensive survey of the DER, power electronics components and their control, electric vehicles (EVs) as load components, and communication and cybersecurity issues are carried out. Second, the role played by AI-based analytics, IoT components along with energy internet architecture, and the BC assistance in improving SG services are thoroughly discussed. This study revealed that AI, IoT, and BC provide automated services to peers by monitoring real-time information about the ESN, thereby enhancing reliability, availability, resilience, stability, security, and sustainability.
Nallapaneni Manoj Kumar; Aneesh A. Chand; Maria Malvoni; Kushal A. Prasad; Kabir A. Mamun; F.R. Islam; Shauhrat S. Chopra. Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids. Energies 2020, 13, 5739 .
AMA StyleNallapaneni Manoj Kumar, Aneesh A. Chand, Maria Malvoni, Kushal A. Prasad, Kabir A. Mamun, F.R. Islam, Shauhrat S. Chopra. Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids. Energies. 2020; 13 (21):5739.
Chicago/Turabian StyleNallapaneni Manoj Kumar; Aneesh A. Chand; Maria Malvoni; Kushal A. Prasad; Kabir A. Mamun; F.R. Islam; Shauhrat S. Chopra. 2020. "Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids." Energies 13, no. 21: 5739.
Finding an appropriate technique to detect an islanding issue is one of the major challenges associated with the design of a resilient grid-linked photovoltaic-based distributed power generation (PV-DPG) system. In general, the technique used for islanding detection must be able to sense the disruptions from the electric grid and quickly disconnect PV-DPG from the grid. The quick disconnection of PV-DPG mostly avoids power quality problems, damage to power assets, voltage stability issues, and frequency instability. In this paper, a new islanding detection technique that is based on tunable Q-factor wavelet transform (TQWT) and an artificial neural network (ANN) is proposed for PV-DPG. The proposed approach consists of two steps: in the first step, the vital detection parameters are computed by performing simulations considering all possible switching transients, islanding events, and faults from the grid side. Then, the decomposition of obtained signals is done using TQWT on different levels. Using the obtained coefficients, at each level, features such as range, minimum, mean, standard deviation, maximum, energy, and log energy entropy are computed. The optimal feature set was selected as the input for the second step. The classification of the non-islanding and islanding states for PV-DPG is made using the ANN classifier in the second step, which achieved an accuracy of 98%. The results representing the efficiency of the proposed approach in noisy and non-noisy environments are also explained. Overall, it is understood that the proposed islanding detection technique would provide suitable insights to detect an islanding issue.
S. Ananda Kumar; M. S. P. Subathra; Nallapaneni Manoj Kumar; Maria Malvoni; N. J. Sairamya; S. Thomas George; Easter S. Suviseshamuthu; Shauhrat S. Chopra. A Novel Islanding Detection Technique for a Resilient Photovoltaic-Based Distributed Power Generation System Using a Tunable-Q Wavelet Transform and an Artificial Neural Network. Energies 2020, 13, 4238 .
AMA StyleS. Ananda Kumar, M. S. P. Subathra, Nallapaneni Manoj Kumar, Maria Malvoni, N. J. Sairamya, S. Thomas George, Easter S. Suviseshamuthu, Shauhrat S. Chopra. A Novel Islanding Detection Technique for a Resilient Photovoltaic-Based Distributed Power Generation System Using a Tunable-Q Wavelet Transform and an Artificial Neural Network. Energies. 2020; 13 (16):4238.
Chicago/Turabian StyleS. Ananda Kumar; M. S. P. Subathra; Nallapaneni Manoj Kumar; Maria Malvoni; N. J. Sairamya; S. Thomas George; Easter S. Suviseshamuthu; Shauhrat S. Chopra. 2020. "A Novel Islanding Detection Technique for a Resilient Photovoltaic-Based Distributed Power Generation System Using a Tunable-Q Wavelet Transform and an Artificial Neural Network." Energies 13, no. 16: 4238.
This article presents performance data concerning a 1MW crystalline photovoltaic (PV) plant installed in the semi-arid climate of India. Data includes the daily average samples from January 2012 to February 2016, related to solar irradiance on the plane of the array, electrical energy injected into the grid, reference yield, final yield, and the performance ratio. Furthermore, the decomposition time series for the performance ratio by applying the classical seasonal decomposition (CSD), Holt-Winters seasonal model (HW), and Seasonal and Trend decomposition using Loess (STL) is also provided for quantifying of the degradation rate of the PV system. The data are provided in the supplementary file included in this article. The dataset is related to the paper entitled “Performance and degradation assessment of large-scale grid-connected solar photovoltaic power plant in tropical semi-arid environment of India.” [1].
Nallapaneni Manoj Kumar; Maria Malvoni; Nikos Hatziargyriou; Shauhrat S Chopra. Data related to crystalline photovoltaic plant performance in the semi-arid climate of India. Data in Brief 2020, 31, 105696 .
AMA StyleNallapaneni Manoj Kumar, Maria Malvoni, Nikos Hatziargyriou, Shauhrat S Chopra. Data related to crystalline photovoltaic plant performance in the semi-arid climate of India. Data in Brief. 2020; 31 ():105696.
Chicago/Turabian StyleNallapaneni Manoj Kumar; Maria Malvoni; Nikos Hatziargyriou; Shauhrat S Chopra. 2020. "Data related to crystalline photovoltaic plant performance in the semi-arid climate of India." Data in Brief 31, no. : 105696.
The performance and degradation of a 1 MWp utility-scale photovoltaic (PV) system located in the tropical semi-arid climate of India is investigated based on four years of monitored data. The reference yield, final yield, system efficiency, capacity factor, and performance ratio are 4.64 h/day 6.23 h/day, 11%, 19.33%, and 74.73%, respectively, according to the standard IEC 61724. The performance is compared to other large-scale PV systems in different climate conditions. The degradation of the PV plant is quantified by using various statistical methods. These methods include the linear least-squares regression (LLS), the classical seasonal decomposition (CSD), the Holt-Winters seasonal model (HW), and the seasonal and trend decomposition using loess (STL). The degradation rate is estimated at 0.27%/year, 0.32%/year, 0.50%/year, and 0.27%/year, respectively, after 50 months operating period. The degradation accuracy analysis classifies the LLS and HW as lower accuracy methods (0.22%) than CSD (0.11%) and STL (0.15%). A comparison of the degradation of mono-Si PV systems for various locations is performed using different statistical methods. This study contributes to the improvements in the knowledge of PV degradation in the Indian climate.
Maria Malvoni; Nallapaneni Manoj Kumar; Shauhrat S Chopra; Nikos Hatziargyriou. Performance and degradation assessment of large-scale grid-connected solar photovoltaic power plant in tropical semi-arid environment of India. Solar Energy 2020, 203, 101 -113.
AMA StyleMaria Malvoni, Nallapaneni Manoj Kumar, Shauhrat S Chopra, Nikos Hatziargyriou. Performance and degradation assessment of large-scale grid-connected solar photovoltaic power plant in tropical semi-arid environment of India. Solar Energy. 2020; 203 ():101-113.
Chicago/Turabian StyleMaria Malvoni; Nallapaneni Manoj Kumar; Shauhrat S Chopra; Nikos Hatziargyriou. 2020. "Performance and degradation assessment of large-scale grid-connected solar photovoltaic power plant in tropical semi-arid environment of India." Solar Energy 203, no. : 101-113.
It is important to investigate the long-term performances of an accurate modeling of photovoltaic (PV) systems, especially in the prediction of output power, with single and double diode models as the configurations mainly applied for this purpose. However, the use of one configuration to model PV panel limits the accuracy of its predicted performances. This paper proposes a new hybrid approach based on classification algorithms in the machine learning framework that combines both single and double models in accordance with the climatic condition in order to predict the output PV power with higher accuracy. Classification trees, k-nearest neighbor, discriminant analysis, Naïve Bayes, support vector machines (SVMs), and classification ensembles algorithms are investigated to estimate the PV power under different conditions of the Mediterranean climate. The examined classification algorithms demonstrate that the double diode model seems more relevant for low and medium levels of solar irradiance and temperature. Accuracy between 86% and 87.5% demonstrates the high potential of the classification techniques in the PV power predicting. The normalized mean absolute error up to 1.5% ensures errors less than those obtained from both single-diode and double-diode equivalent-circuit models with a reduction up to 0.15%. The proposed hybrid approach using machine learning (ML) algorithms could be a key solution for photovoltaic and industrial software to predict more accurate performances.
Malvoni Maria; Chaibi Yassine. Machine Learning Based Approaches for Modeling the Output Power of Photovoltaic Array in Real Outdoor Conditions. Electronics 2020, 9, 315 .
AMA StyleMalvoni Maria, Chaibi Yassine. Machine Learning Based Approaches for Modeling the Output Power of Photovoltaic Array in Real Outdoor Conditions. Electronics. 2020; 9 (2):315.
Chicago/Turabian StyleMalvoni Maria; Chaibi Yassine. 2020. "Machine Learning Based Approaches for Modeling the Output Power of Photovoltaic Array in Real Outdoor Conditions." Electronics 9, no. 2: 315.
Life cycle metrics evolution specific to the climate zone of photovoltaic (PV) operation would give detailed insights on the environmental and economic performance. At present, vast literature is available on the PV life cycle metrics where only the output energies ignoring the degradation rate (DR) influence. In this study, the environ-economic analysis of three PV technologies, namely, multi-crystalline silicon (mc-Si), amorphous silicon (a-Si) and hetero-junction with an intrinsic thin layer (HIT) have been carried out in identical environmental conditions. The energy performance parameters and the DR rate of three PV technologies are evaluated based on the monitored real time data from the installation site in hot semi-arid climates. The assessment demonstrates that the HIT PV module technology exhibits more suitable results compared to mc-Si and a-Si PV systems in hot semi-arid climatic conditions of India. Moreover, energy metrices which includes energy payback time (EPBT), energy production factor (EPF) and life cycle conversion efficiency (LCCE) of the HIT technologies are found to be 1.0, 24.93 and 0.15 years, respectively. HIT PV system has higher potential to mitigate the CO2 and carbon credit earned compared to mc-Si and a-Si PV system under hot semi-arid climate. However, the annualized uniform cost (UAC) for mc-Si (3.60 Rs/kWh) and a-Si (3.40 Rs/kWh) are more admissible in relation to the HIT (6.63 Rs/kWh) PV module type. We conclude that the approach of considering DR influenced life cycle metrics over the traditional approach can support to identify suitable locations for specific PV technology.
Pramod Rajput; Maria Malvoni; Nallapaneni Manoj Kumar; O. S. Sastry; ArunKumar Jayakumar. Operational Performance and Degradation Influenced Life Cycle Environmental–Economic Metrics of mc-Si, a-Si and HIT Photovoltaic Arrays in Hot Semi-arid Climates. Sustainability 2020, 12, 1075 .
AMA StylePramod Rajput, Maria Malvoni, Nallapaneni Manoj Kumar, O. S. Sastry, ArunKumar Jayakumar. Operational Performance and Degradation Influenced Life Cycle Environmental–Economic Metrics of mc-Si, a-Si and HIT Photovoltaic Arrays in Hot Semi-arid Climates. Sustainability. 2020; 12 (3):1075.
Chicago/Turabian StylePramod Rajput; Maria Malvoni; Nallapaneni Manoj Kumar; O. S. Sastry; ArunKumar Jayakumar. 2020. "Operational Performance and Degradation Influenced Life Cycle Environmental–Economic Metrics of mc-Si, a-Si and HIT Photovoltaic Arrays in Hot Semi-arid Climates." Sustainability 12, no. 3: 1075.
The alleged reliability has led the longest warranty period for Photovoltaic (PV) modules up to 20–25 years; it becomes possible after understanding the failure mode and degradation analysis of PV module. Failure mode decreases the performance of the PV module throughout the long-term outdoor exposure. The main objective of the present study is to identify the failure mechanism and failure mode of solar PV modules and their impact on degradation in operating conditions. Assessment of previous studies on rate indicates the highest performance losses at initial stage of outdoor exposure and a degradation drop-off of 0.014% per year. In this context, risk priority number (RPN) analysis is carried out to identify the severity of the failure mode, which affect the system performance for c-Si technologies. However, hot spot and de-lamination are degradation modes related to safety issue with lower value of RPN <50.
Pramod Rajput; Maria Malvoni; Nallapaneni Manoj Kumar; O.S. Sastry; G.N. Tiwari. Risk priority number for understanding the severity of photovoltaic failure modes and their impacts on performance degradation. Case Studies in Thermal Engineering 2019, 16, 100563 .
AMA StylePramod Rajput, Maria Malvoni, Nallapaneni Manoj Kumar, O.S. Sastry, G.N. Tiwari. Risk priority number for understanding the severity of photovoltaic failure modes and their impacts on performance degradation. Case Studies in Thermal Engineering. 2019; 16 ():100563.
Chicago/Turabian StylePramod Rajput; Maria Malvoni; Nallapaneni Manoj Kumar; O.S. Sastry; G.N. Tiwari. 2019. "Risk priority number for understanding the severity of photovoltaic failure modes and their impacts on performance degradation." Case Studies in Thermal Engineering 16, no. : 100563.
The presented data are related to the article “Solar Irradiance and Temperature Influence on the Photovoltaic Cell Equivalent-Circuit Models” [1]. Data include the open-circuit voltage, the short-circuit current and the output power of the Shell SM55 mono-crystalline Photovoltaic (PV) Solar Module obtained from a PV panel modelling based on the single-diode and the double-diode circuit models, coupled with Chaibi and Ishaque parameter extraction techniques [2], [3]. The I-V curves as simulation results are provided at various levels of solar irradiance and temperature.
Yassine Chaibi; Maria Malvoni; Amine Allouhi; Salhi Mohamed. Data on the I–V characteristics related to the SM55 monocrystalline PV module at various solar irradiance and temperatures. Data in Brief 2019, 26, 104527 .
AMA StyleYassine Chaibi, Maria Malvoni, Amine Allouhi, Salhi Mohamed. Data on the I–V characteristics related to the SM55 monocrystalline PV module at various solar irradiance and temperatures. Data in Brief. 2019; 26 ():104527.
Chicago/Turabian StyleYassine Chaibi; Maria Malvoni; Amine Allouhi; Salhi Mohamed. 2019. "Data on the I–V characteristics related to the SM55 monocrystalline PV module at various solar irradiance and temperatures." Data in Brief 26, no. : 104527.
The paper presents a spatio-temporal forecasting for the photovoltaic (PV) power generation by combining the three-dimensional wavelet transform (3D-DWT) and the Least Square Support Vector Machines (LS-SVM) to deal with historical time series data of distributed PV plants in both spatial and temporal domain. The proposed forecasting method applies the wavelet decomposition to the PV power data collected from several PV installations in a three-dimensional space taking into account the spatial distribution of the PV locations and the related PV output power in a defined time framework. The wavelet decomposition output is then used as input for a forecasting model based on the LS-SVM to predict the solar PV power of each plant. A case study is presented using hourly time series of 9 PV installations located in the Greek island of Rhodes in order to predict the power generation of each individual PV plant at 24 hours-ahead time horizon. The forecast performance of the proposed approach is investigated by error metrics and compared with a prediction model based on simple LS-SVM to quantify the improvements achieved by the proposed method.
Maria Malvoni; Nikos Hatziargyriou. One-day ahead PV power forecasts using 3D Wavelet Decomposition. 2019 International Conference on Smart Energy Systems and Technologies (SEST) 2019, 1 -6.
AMA StyleMaria Malvoni, Nikos Hatziargyriou. One-day ahead PV power forecasts using 3D Wavelet Decomposition. 2019 International Conference on Smart Energy Systems and Technologies (SEST). 2019; ():1-6.
Chicago/Turabian StyleMaria Malvoni; Nikos Hatziargyriou. 2019. "One-day ahead PV power forecasts using 3D Wavelet Decomposition." 2019 International Conference on Smart Energy Systems and Technologies (SEST) , no. : 1-6.
M. Malvoni; A. Leggieri; G. Maggiotto; P.M. Congedo; M.G. De Giorgi. Corrigendum To Long Term Performance, Losses And Efficiency Analysis Of A 960 Kwp Photovoltaic System In The Mediterranean Climate [Energy Conversion And Management 145 (2017) 169–181]. Energy Conversion and Management 2018, 159, 413 .
AMA StyleM. Malvoni, A. Leggieri, G. Maggiotto, P.M. Congedo, M.G. De Giorgi. Corrigendum To Long Term Performance, Losses And Efficiency Analysis Of A 960 Kwp Photovoltaic System In The Mediterranean Climate [Energy Conversion And Management 145 (2017) 169–181]. Energy Conversion and Management. 2018; 159 ():413.
Chicago/Turabian StyleM. Malvoni; A. Leggieri; G. Maggiotto; P.M. Congedo; M.G. De Giorgi. 2018. "Corrigendum To Long Term Performance, Losses And Efficiency Analysis Of A 960 Kwp Photovoltaic System In The Mediterranean Climate [Energy Conversion And Management 145 (2017) 169–181]." Energy Conversion and Management 159, no. : 413.
Maria Malvoni; Paolo Maria Congedo; Domenico Laforgia. Analysis of energy consumption: a case study of an Italian winery. Energy Procedia 2017, 126, 227 -233.
AMA StyleMaria Malvoni, Paolo Maria Congedo, Domenico Laforgia. Analysis of energy consumption: a case study of an Italian winery. Energy Procedia. 2017; 126 ():227-233.
Chicago/Turabian StyleMaria Malvoni; Paolo Maria Congedo; Domenico Laforgia. 2017. "Analysis of energy consumption: a case study of an Italian winery." Energy Procedia 126, no. : 227-233.
Maria Malvoni; Maria Grazia De Giorgi; Paolo Maria Congedo. Forecasting of PV Power Generation using weather input data‐preprocessing techniques. Energy Procedia 2017, 126, 651 -658.
AMA StyleMaria Malvoni, Maria Grazia De Giorgi, Paolo Maria Congedo. Forecasting of PV Power Generation using weather input data‐preprocessing techniques. Energy Procedia. 2017; 126 ():651-658.
Chicago/Turabian StyleMaria Malvoni; Maria Grazia De Giorgi; Paolo Maria Congedo. 2017. "Forecasting of PV Power Generation using weather input data‐preprocessing techniques." Energy Procedia 126, no. : 651-658.
Maria Malvoni; Maria Grazia De Giorgi; Paolo Maria Congedo. Study of degradation of a grid connected photovoltaic system. Energy Procedia 2017, 126, 644 -650.
AMA StyleMaria Malvoni, Maria Grazia De Giorgi, Paolo Maria Congedo. Study of degradation of a grid connected photovoltaic system. Energy Procedia. 2017; 126 ():644-650.
Chicago/Turabian StyleMaria Malvoni; Maria Grazia De Giorgi; Paolo Maria Congedo. 2017. "Study of degradation of a grid connected photovoltaic system." Energy Procedia 126, no. : 644-650.
M. Malvoni; A. Leggieri; G. Maggiotto; Paolo Maria Congedo; Maria Grazia De Giorgi. Long term performance, losses and efficiency analysis of a 960 kW P photovoltaic system in the Mediterranean climate. Energy Conversion and Management 2017, 145, 169 -181.
AMA StyleM. Malvoni, A. Leggieri, G. Maggiotto, Paolo Maria Congedo, Maria Grazia De Giorgi. Long term performance, losses and efficiency analysis of a 960 kW P photovoltaic system in the Mediterranean climate. Energy Conversion and Management. 2017; 145 ():169-181.
Chicago/Turabian StyleM. Malvoni; A. Leggieri; G. Maggiotto; Paolo Maria Congedo; Maria Grazia De Giorgi. 2017. "Long term performance, losses and efficiency analysis of a 960 kW P photovoltaic system in the Mediterranean climate." Energy Conversion and Management 145, no. : 169-181.
M. Malvoni; M.C. Fiore; G. Maggiotto; L. Mancarella; R. Quarta; V. Radice; Paolo Maria Congedo; M.G. De Giorgi. Improvements in the predictions for the photovoltaic system performance of the Mediterranean regions. Energy Conversion and Management 2016, 128, 191 -202.
AMA StyleM. Malvoni, M.C. Fiore, G. Maggiotto, L. Mancarella, R. Quarta, V. Radice, Paolo Maria Congedo, M.G. De Giorgi. Improvements in the predictions for the photovoltaic system performance of the Mediterranean regions. Energy Conversion and Management. 2016; 128 ():191-202.
Chicago/Turabian StyleM. Malvoni; M.C. Fiore; G. Maggiotto; L. Mancarella; R. Quarta; V. Radice; Paolo Maria Congedo; M.G. De Giorgi. 2016. "Improvements in the predictions for the photovoltaic system performance of the Mediterranean regions." Energy Conversion and Management 128, no. : 191-202.
The power forecasting plays a significant role in the electrical systems. Furthermore the high-dimensional data reduction without losing essential information represents an important advantage in the forecasting models. Low computational costs and short execution time together with high predicted performance are the main goals to be reached in the development of a prediction method. In this paper a hybrid method based on an active selection of the support vectors, using the quadratic Renyi entropy criteria in combination with the principal component analysis (PCA), is shown to dimensionally reduce the training data in the forecasting models. The reduced data have been used to implement the Least Squares Support Vector Machines (LS-SVM) in order to predict the photovoltaic (PV) power in the day-ahead time horizon. The model has been validated using historical data of a PV system in the Mediterranean climate. Additionally the weather variations have been taken into account to evaluate the outcome of the sunny and cloudy condition in the PV forecasting models. The proposed technique gives fulfill results. A training data size same as 30% original dimension allows to improve the forecasting accuracy and reduces the computational time of 70% respect to an implementation without dimensionality reduction data.
M. Malvoni; M.G. De Giorgi; Paolo Maria Congedo. Photovoltaic forecast based on hybrid PCA–LSSVM using dimensionality reducted data. Neurocomputing 2016, 211, 72 -83.
AMA StyleM. Malvoni, M.G. De Giorgi, Paolo Maria Congedo. Photovoltaic forecast based on hybrid PCA–LSSVM using dimensionality reducted data. Neurocomputing. 2016; 211 ():72-83.
Chicago/Turabian StyleM. Malvoni; M.G. De Giorgi; Paolo Maria Congedo. 2016. "Photovoltaic forecast based on hybrid PCA–LSSVM using dimensionality reducted data." Neurocomputing 211, no. : 72-83.
Maria Malvoni; Cristina Baglivo; Paolo Maria Congedo; Domenico Laforgia. CFD modeling to evaluate the thermal performances of window frames in accordance with the ISO 10077. Energy 2016, 111, 430 -438.
AMA StyleMaria Malvoni, Cristina Baglivo, Paolo Maria Congedo, Domenico Laforgia. CFD modeling to evaluate the thermal performances of window frames in accordance with the ISO 10077. Energy. 2016; 111 ():430-438.
Chicago/Turabian StyleMaria Malvoni; Cristina Baglivo; Paolo Maria Congedo; Domenico Laforgia. 2016. "CFD modeling to evaluate the thermal performances of window frames in accordance with the ISO 10077." Energy 111, no. : 430-438.
The data concern the photovoltaic (PV) power, forecasted by a hybrid model that considers weather variations and applies a technique to reduce the input data size, as presented in the paper entitled “Photovoltaic forecast based on hybrid pca-lssvm using dimensionality reducted data” (M. Malvoni, M.G. De Giorgi, P.M. Congedo, 2015) [1]. The quadratic Renyi entropy criteria together with the principal component analysis (PCA) are applied to the Least Squares Support Vector Machines (LS-SVM) to predict the PV power in the day-ahead time frame. The data here shared represent the proposed approach results. Hourly PV power predictions for 1,3,6,12, 24 ahead hours and for different data reduction sizes are provided in Supplementary material.
M. Malvoni; M.G. De Giorgi; P.M. Congedo. Data on Support Vector Machines (SVM) model to forecast photovoltaic power. Data in Brief 2016, 9, 13 -16.
AMA StyleM. Malvoni, M.G. De Giorgi, P.M. Congedo. Data on Support Vector Machines (SVM) model to forecast photovoltaic power. Data in Brief. 2016; 9 ():13-16.
Chicago/Turabian StyleM. Malvoni; M.G. De Giorgi; P.M. Congedo. 2016. "Data on Support Vector Machines (SVM) model to forecast photovoltaic power." Data in Brief 9, no. : 13-16.