• Energy Research

Abstract Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. They have been used to solve complicated practical problems in various areas and are becoming more and more popular nowadays. AI-techniques have the following features: can learn from examples; are fault tolerant in the sense that they are able to handle noisy and incomplete data; are able to deal with non-linear problems; and once trained can perform prediction and generalization at high speed. AI-based systems are being developed and deployed worldwide in a myriad of applications, mainly because of their symbolic reasoning, flexibility and explanation capabilities. AI have been used and applied in different sectors, such as engineering, economics, medicine, military, marine, etc. They have also been applied for modeling, identification, optimization, prediction, forecasting, and control of complex systems. The main objective of this paper is to present an overview of the AI-techniques for sizing photovoltaic (PV) systems: stand-alone PVs, grid-connected PV systems, PV-wind hybrid systems, etc. Published literature presented in this paper show the potential of AI as a design tool for the optimal sizing of PV systems. Additionally, the advantage of using an AI-based sizing of PV systems is that it provides good optimization, especially in isolated areas, where the weather data are not always available.

Abstract In this paper an FPGA-based implementation of a real time perturb and observe (P&O) algorithm for tracking the Maximum Power Point (MPP) of a photovoltaic (PV) generator is presented. The P&O algorithm has been designed using the very high-speed description language (VHDL) and implemented on Xilinx Virtex-II-Pro(xc2v1000-4fg456) - Field Programmable Gate Array (FPGA). The algorithm and the hardware have been simulated and tested by conditioning the power produced by the PV-modules installed on the rooftop of the “Hall of Technology Laboratory” at Jijel University. The main advantages of the developed MPPT are low cost, good velocity, acceptable reliability, and easy implementation. However, its main disadvantage is related to the fact that for fast changes in irradiance it may fail to track the maximum power point. The efficiency of the implemented P&O controller is about 96%.

Abstract Due to the various seasonal, monthly and daily changes in meteorological data, it is relatively difficult to find a suitable model for Photovoltaic power supply (PVPS) system. This paper deals with the modelling and simulation of a PVPS system using an Adaptive Neuro-Fuzzy Inference Scheme (ANFIS) and the proposition of a new expert configuration PVPS system. For the modelling of the PVPS system, it is required to find suitable models for its different components (ANFIS PV generator, ANFIS battery and ANFIS regulator) that could give satisfactory results under variable climatic conditions in order to test its performance and reliability. A database of measured climate data (global radiation, temperature and humidity) and electrical data (photovoltaic, battery and regulator voltage and current) of a PVPS system installed in Tahifet (south of Algeria) has been recorded for the period from 1992 to 1997. These data have been used for the modelling and simulation of the PVPS system. The results indicated that the reliability and the accuracy of the simulated system are excellent and the correlation coefficient between measured values and those estimated by the ANFIS gave a good prediction accuracy of 98%. Additionally, test results show that the ANFIS performed better than the Artificial Neural Network (ANN), which has also being tried to model the system. In addition, a new configuration of an expert PVPS system is proposed in this work. The predicted electrical data by the ANFIS model can be used for several applications in PV systems.

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 analysis, but overall a less stable performance (NMAE% > 2% and up to 5.3% for all the other cases). For cloudy days, the forecasting performance of both methods typically drops; the performance is rather stable for the method that does not use weather forecasts, while for the hybrid method it varies significantly for the days considered in the analysis.

In this paper, the applications of artificial intelligence-based methods for tracking the maximum power point have been reviewed and analysed. The reviewed methods are based upon neural networks, fuzzy logic, evolutionary algorithms, which include genetic algorithms, particle swarm optimization, ant colony optimization, and other hybrid methods. Rapid advances in programmable logic devices (PLDs) including field programmable gate arrays (FPGAs) give good opportunities to integrate efficiently such techniques for real time applications. An attempt is made to highlight the future trends and challenges in the development of embedded intelligent digital maximum power point tracking (MPPT) controllers into FPGA chip. Special attention is also given to the cost, complexity of implementation, efficiency, and possible practical realization. We believe that this review provides valuable information for engineers, designers and scientist working in this area and show future trends in the development of embedded intelligent techniques for renewable energy systems.

To support farmers and improve the quality of crops production, designing of smart greenhouses is becoming indispensable. In this paper, a novel prototype for remote monitoring of a greenhouse is designed. The prototype allows creating an adequate artificial environment inside the greenhouse (e.g., water irrigation, ventilation, light intensity, and CO2 concentration). Thanks to the Internet of things technique, the parameters controlled (air temperature, relative humidity, capacitive soil moisture, light intensity, and CO2 concentration) were measured and uploaded to a designed webpage using appropriate sensors with a low-cost Wi-Fi module (NodeMCU V3). An Android mobile application was also developed using an A6 GSM module for notifying farmers (e.g., sending a warning message in case of any anomaly) regarding the state of the plants. A low-cost camera was used to collect and send images of the plants via the webpage for possible diseases identification and classification. In this context, a deep learning convolutional neural network was developed and implemented into a Raspberry Pi 4. To supply the prototype, a small-scale photovoltaic system was built. The experimental results showed the feasibility and demonstrated the ability of the prototype to monitor and control the greenhouse remotely, as well as to identify the state of the plants. The designed smart prototype can offer real-time remote measuring and sensing services to farmers.

Abstract Concentrator photovoltaics (CPV) is considered to be one of the most promising renewable energy components that could lead to a reduction on the dependence on fossil fuels. The aim of CPV technology is to lower the cost of the system by reducing the semiconductor material, and replacing it by cheap optical devices that concentrate the light received from the sun on a small-size solar cell. The electrical characterization of devices based on this technology however, is inherently different and more complex than that of the traditional PV devices. Due to the advantages offered by the Artificial Neuron Networks (ANNs) to solve complex and non-linear problems, and the great level of complexity of electrical modelling of CPV devices, in recent years, several authors have applied a variety of ANNs to solve issues related to CPV technology. In this paper, a review of the ANNs developed to address various topics related with both, low and high concentrator photovoltaics, is presented. Moreover, a review of the ANN-based models to predict the main environmental parameters that affect the performance of CPV systems operating outdoors is also provided. Published papers presented show the potential of the ANNs as a powerful tool for modelling the CPV technology.

Abstract Forecasting of solar irradiance is in general significant for planning the operations of power plants which convert renewable energies into electricity. In particular, the possibility to predict the solar irradiance (up to 24 h or even more) can became – with reference to the Grid Connected Photovoltaic Plants (GCPV) – fundamental in making power dispatching plans and – with reference to stand alone and hybrid systems – also a useful reference for improving the control algorithms of charge controllers. In this paper, a practical method for solar irradiance forecast using artificial neural network (ANN) is presented. The proposed Multilayer Perceptron MLP-model makes it possible to forecast the solar irradiance on a base of 24 h using the present values of the mean daily solar irradiance and air temperature. An experimental database of solar irradiance and air temperature data (from July 1st 2008 to May 23rd 2009 and from November 23rd 2009 to January 24th 2010) has been used. The database has been collected in Trieste (latitude 45°40′N, longitude 13°46′E), Italy. In order to check the generalization capability of the MLP-forecaster, a K -fold cross-validation was carried out. The results indicate that the proposed model performs well, while the correlation coefficient is in the range 98–99% for sunny days and 94–96% for cloudy days. As an application, the comparison between the forecasted one and the energy produced by the GCPV plant installed on the rooftop of the municipality of Trieste shows the goodness of the proposed model.

Maximum power point tracking (MPPT) must usually be integrated with photovoltaic (PV) power systems so that the photovoltaic arrays are able to deliver the maximum power available. In this paper details of the work, carried out to optimize and implement a fuzzy logic controller (FLC) used as a maximum-power-point tracker for a stand-alone PV system, are presented. The near optimum design for membership functions and control rules were found simultaneously by genetic algorithms (GAs) which are search algorithms based on the mechanism of natural selection and genetics. These are easy to implement and efficient for multivariable optimization problems such as in fuzzy controller design. The FLC thus designed, as well as the components of the PV control unit, were implemented efficiently on a Xilinx reconfigurable field-programmable gate array (FPGA) chip using VHDL Hardware Description Language. The obtained simulation results confirm the good tracking efficiency and rapid response to changes in environmental parameters.