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This work proposes and evaluates a method for the nowcasting of solar irradiance variability in multiple time horizons, namely 5, 10, and 15 min ahead. The method is based on a Convolutional Neural Network structure that exploits infrared... more
This work proposes and evaluates a method for the nowcasting of solar irradiance variability in multiple time horizons, namely 5, 10, and 15 min ahead. The method is based on a Convolutional Neural Network structure that exploits infrared sky images acquired through an All-Sky Imager to estimate the range of possible values that the Clear-Sky Index will possibly assume over a selected forecast horizon. All data available, from the infrared images to the measurements of Global Horizontal Irradiance (necessary in order to compute Clear-Sky Index), are acquired at SolarTechLAB in Politecnico di Milano. The proposed method demonstrated a discrete performance level, with an accuracy peak for the 5 min time horizon, where about 65% of the available samples are attributed to the correct range of Clear-Sky Index values.
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The increasing energy demand all over the world is pushing new research efforts on renewable energy sources (RES), especially in terms of system performance and grid interfacing. Artificial intelligence based forecasting method can... more
The increasing energy demand all over the world is pushing new research efforts on renewable energy sources (RES), especially in terms of system performance and grid interfacing. Artificial intelligence based forecasting method can support operators with useful tools both in energy production and consumption management. This paper propose an in depth study on Artificial Neural Networks (ANN) effectiveness, by evaluating different tool structures for the next-day energy forecasting of multiple PV plants, with the aim to evaluate its sensitivity as a function of the data sets in input, number of neurons, and training set selection strategy. Different weather conditions will be taken into account to test at the same time the algorithm robustness and the effects of particular events on the forecasting output. The study is based on a previous experimental activities carried out by the research group evaluating technical and economic reasons to develop power forecasting models for large g...
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This work proposes a methodology for the optimal training of rule-based management strategies, to be directly implemented in the industrial controller of hybrid off-grid microgrids. The parameters defining the control rules are optimally... more
This work proposes a methodology for the optimal training of rule-based management strategies, to be directly implemented in the industrial controller of hybrid off-grid microgrids. The parameters defining the control rules are optimally tuned resorting to different evolutionary algorithms, based on the expected operating conditions. The performance of the resulting management heuristics is compared with conventional approaches to optimal scheduling, including Mixed Integer Linear Programming (MILP) optimization, direct evolutionary scheduling optimization, and traditional non-trained heuristics. Results show how the trained heuristics achieve a performance very close to the global optimum found by the MILP solution, outperforming the other methods, and providing a single-layer commitment and dispatch algorithm which is easily deployable in the microgrid controller.
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Microgrids represent a flexible way to integrate renewable energy sources with programmable generators and storage systems. In this regard, a synergic integration of those sources is crucial to minimize the operating cost of the microgrid... more
Microgrids represent a flexible way to integrate renewable energy sources with programmable generators and storage systems. In this regard, a synergic integration of those sources is crucial to minimize the operating cost of the microgrid by efficient storage management and generation scheduling. The forecasts of renewable generation can be used to attain optimal management of the controllable units by predictive optimization algorithms. This paper introduces the implementation of a two-layer hierarchical energy management system for islanded photovoltaic microgrids. The first layer evaluates the optimal unit commitment, according to the photovoltaic forecasts, while the second layer deals with the power-sharing in real time, following as close as possible the daily schedule provided by the upper layer while balancing the forecast errors. The energy management system is experimentally tested at the Multi-Good MicroGrid Laboratory under three different photovoltaic forecast models: (...
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Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate photovoltaic output forecasters remains a challenging issue, particularly for multistep-ahead prediction.... more
Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate photovoltaic output forecasters remains a challenging issue, particularly for multistep-ahead prediction. Accurate PV output power forecasting is critical in a number of applications, such as micro-grids (MGs), energy optimization and management, PV integrated in smart buildings, and electrical vehicle chartering. Over the last decade, a vast literature has been produced on this topic, investigating numerical and probabilistic methods, physical models, and artificial intelligence (AI) techniques. This paper aims at providing a complete and critical review on the recent applications of AI techniques; we will focus particularly on machine learning (ML), deep learning (DL), and hybrid methods, as these branches of AI are becoming increasingly attractive. Special attention will be paid to the recent development of the application of DL, as well as to the future...
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Forecasting the power production from renewable energy sources (RESs) has become fundamental in microgrid applications to optimize scheduling and dispatching of the available assets. In this article, a methodology to provide the 24 h... more
Forecasting the power production from renewable energy sources (RESs) has become fundamental in microgrid applications to optimize scheduling and dispatching of the available assets. In this article, a methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented. The goal of this paper is to provide a robust methodology to forecast 24 h in advance the PV power production in a microgrid, addressing the specific criticalities of this environment. The proposed approach has to validate measured data properly, through an effective algorithm and further refine the power forecast when newer data are available. The procedure is fully implemented in a facility of the Multi-Good Microgrid Laboratory (MG L a b 2 ) of the Politecnico di Milano, Milan, Italy, where new Energy Management Systems (EMSs) are studied. Reported results validate the proposed approach as a robust and accurate procedure fo...
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We compare the 24-hour ahead forecasting performance of two methods commonly used for the prediction of the power output of photovoltaic systems. Both methods are based on Artificial Neural Networks (ANN), which have been trained on the... more
We compare the 24-hour ahead forecasting performance of two methods commonly used for the prediction of the power output of photovoltaic systems. Both methods are based on Artificial Neural Networks (ANN), which have been trained on the same dataset, thus enabling a much-needed homogeneous comparison currently lacking in the available literature. The dataset consists of an hourly series of simultaneous climatic and PV system parameters covering an entire year, and has been clustered to distinguish sunny from cloudy days and separately train the ANN. One forecasting method feeds only on the available dataset, while the other is a hybrid method as it relies upon the daily weather forecast. For sunny days, the first method shows a very good and stable prediction performance, with an almost constant Normalized Mean Absolute Error, NMAE%, in all cases (1% < NMAE% < 2%); the hybrid method shows an even better performance (NMAE% < 1%) for two of the days considered in this analysi...
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Photovoltaics, among renewable energy sources (RES), has become more popular [...]
Research Interests: Engineering, Computer Science, Evolutionary algorithms, Computational Intelligence, Embedded Systems, and 15 moreGenetic Algorithm, Ensemble Methods, Demand Response, Artificial Neural Networks, Battery, Orientation, Applied Sciences, Firefly Algorithm, Metaheuristic, Electrical Parameters, Analytical Methods, MPPT Algorithm, Monitoring System, Metaheuristic Algorithm, and Artificial Neural Network
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In this paper a forecasting method for the Next Day's energy production forecast is proposed with respect to photovoltaic plants. A new hybrid method PHANN (Physical Hybrid Artificial Neural Network) based on Artificial Neural Network... more
In this paper a forecasting method for the Next Day's energy production forecast is proposed with respect to photovoltaic plants. A new hybrid method PHANN (Physical Hybrid Artificial Neural Network) based on Artificial Neural Network (ANN) and basic Physical constraints of the PV plant, is presented and compared with an ANN standard method. Furthermore, the accuracy of the two methods have been studied in order to better understand the intrinsic error committed by the PHANN, reporting some numerical results. This computing-based hybrid approach is proposed for PV energy forecasting in view of optimal usage and management of RES in future smart grid applications.
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Renewable energy penetration has been greatly increasing in these years and photovoltaic (PV) energy seems to be one of the main renewable source, widely and easily available. To valuate with good accuracy PV energy production usually... more
Renewable energy penetration has been greatly increasing in these years and photovoltaic (PV) energy seems to be one of the main renewable source, widely and easily available. To valuate with good accuracy PV energy production usually designers need complicate software tools. In this paper a simple method to estimate the PV plant Yearly Energy production is presented. The proposed method employs only the available data of the PV plant (as location and PV nominal power). The analytical method presented here can be a profitable tool for design engineers in planning PV considering different locations.
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In these years the energy production by renewable sources is becoming very important and the photovoltaic (PV) energy seems to be the main renewable source, widely available and easily exploitable. In this context it is necessary to find... more
In these years the energy production by renewable sources is becoming very important and the photovoltaic (PV) energy seems to be the main renewable source, widely available and easily exploitable. In this context it is necessary to find correct tools to optimize their energy production. This paper presents a new analytical method to estimate the optimal size of a grid connected PV plant linked up to a specific inverter. The derived analytical expressions contain only the available data of the inverter (as efficiency, nominal power, etc...) and of the PV plant (as location and PV nominal power).
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High penetration of solar and wind power in the electricity system provides a number of challenges to the grid such as grid stability and security, system operation, and market economics. Ones of the considerable problems of solar and... more
High penetration of solar and wind power in the electricity system provides a number of challenges to the grid such as grid stability and security, system operation, and market economics. Ones of the considerable problems of solar and wind systems, they depend on the weather, as compared to the conventional generation. As we know, the balance in managing load and generated power in energy system is very important. If the power which is supplied from solar and wind perfectly predictable, the extra cost of operating power system with a large penetration of renewable energy will be reduced. Since, the accurate and reliable forecasting system for renewable sources represents an important topic as a major contribution for increasing non-programmable renewable on over the world. The target of this research is to describes the advanced hybrid evolutionary techniques of computational intelligence applied for PV as well as wind power forecast. The evaluation of this investigation is obtained by comparing different definitions of the forecasting error. Moreover, the meaning of NWP (numerical weather prediction) values based on meteorological information on solar and wind power forecasting at Italy has been highlighted in this research.
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ABSTRACT In this paper a direct comparison between two different PV panel technologies is presented. The calculation of PR, after one year of analytic monitoring of a PV plant, allows some comparative considerations of special interest.... more
ABSTRACT In this paper a direct comparison between two different PV panel technologies is presented. The calculation of PR, after one year of analytic monitoring of a PV plant, allows some comparative considerations of special interest. By comparing the performance between CdTe modules and the polycrystalline silicon ones, put in equal environmental and plant conditions, it has resulted a lower annual average PR of the latter than the one of the former.