A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module

The design process of photovoltaic (PV) modules can be greatly enhanced by using advanced and accurate models in order to predict accurately their electrical output behavior. The main aim of this paper is to investigate the application of an advanced neural network based model of a module to improve the accuracy of the predicted output I--V and P--V curves and to keep in account the change of all the parameters at different operating conditions. Radial basis function neural networks (RBFNN) are here utilized to predict the output characteristic of a commercial PV module, by reading only the data of solar irradiation and temperature. A lot of available experimental data were used for the training of the RBFNN, and a backpropagation algorithm was employed. Simulation and experimental validation is reported

Assessment of the photovoltaic potential at urban level based on 3D city models: A case study and new methodological approach

The use of 3D city models combined with simulation functionalities allows to quantify energy demand and renewable generation for a very large set of buildings. The scope of this paper is to determine the solar photovoltaic potential at an urban and regional scale using CityGML geometry descriptions of every building. An innovative urban simulation platform is used to calculate the PV potential of the Ludwigsburg County in south-west Germany, in which every building was simulated by using 3D city models. Both technical and economic potential (considering roof area and insolation thresholds) are investigated, as well as two different PV efficiency scenarios. In this way, it was possible to determine the fraction of the electricity demand that can be covered in each municipality and the whole region, deciding the best strategy, the profitability of the investments and determining optimal locations. Additionally, another important contribution is a literature review regarding the different methods of PV potential estimation and the available roof area reduction coefficients. An economic analysis and emission assessment has also been developed. The results of the study show that it is possible to achieve high annual rates of covered electricity demand in several municipalities for some of the considered scenarios, reaching even more than 100% in some cases. The use of all available roof space (technical potential) could cover 77% of the region’s electricity consumption and 56% as an economic potential with only high irradiance roofs considered. The proposed methodological approach should contribute valuably in helping policy-making processes and communicating the advantages of distributed generation and PV systems in buildings to regulators, researchers and the general public

Overview of methods to analyse dynamic data

This book gives an overview of existing data analysis methods to analyse the dynamic data obtained from full scale testing, with their advantages and drawbacks. The overview of full scale testing and dynamic data analysis is limited to energy performance characterization of either building components or whole buildings. The methods range from averaging and regression methods to dynamic approaches based on system identification techniques. These methods are discussed in relation to their application in following in situ measurements: -measurement of thermal transmittance of building components based on heat flux meters; -measurement of thermal and solar transmittance of building components tested in outdoor calorimetric test cells; -measurement of heat transfer coefficient and solar aperture of whole buildings based on co-heating or transient heating tests; -characterisation of the energy performance of whole buildings based on energy use monitoring

Estimating solar radiation in Ikeja and Port Harcourt via correlation with relative humidity and temperature

This paper is part of the Proceedings of the 6th International Conference on Energy and Sustainability (ESUS 2015). http://www.witconferences.comRelative humidity and temperature data are more readily available to obtain from observatories than sunshine hour data. In this work, 10 years (1986–1987, 1990–1997) monthly average measurement of relative solar radiation, daily temperature range, relative humidity and the ratio of minimum to maximum temperature were used to establish the coefficient of eight models for estimating solar radiation in Ikeja and Port Harcourt. Coefficient of correlation (R), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t-statistic and the rank score were used as performance indicators. In Port Harcourt, the equation producing the best result with MBE, RMSE, MPE and t-statistic value of −0.1078, 0.9850, −0.4373% and 0.3653, respectively, is given by: Rs/Ro = 3.266 − 0.306(RH)0,5. In Ikeja, the equation producing the best estimation with MBE, RMSE, MPE and t-statistic value of 0.1590, 1.0110, 2.0559% and 0.5281, respectively, is given by: Rs/Ro = 2.042 − 2.136(θ)

Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation. One of the systems where these techniques can be applied with massive potential impact are the legacy monitoring systems existing in solar PV energy generation plants. These systems produce a great amount of data over time, while at the same time they demand an important e ort in order to increase their performance through the use of more accurate predictive analytics to reduce production losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of the problems to address. This paper presents a review and a comparative analysis of six intelligent optimization modelling techniques, which have been applied on a PV plant case study, using the energy production forecast as the decision variable. The methodology proposed not only pretends to elicit the most accurate solution but also validates the results, in comparison with the di erent outputs for the di erent techniques

Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models

In the field of renewable energy, reliability analysis techniques combining the operating time of the system with the observation of operational and environmental conditions, are gaining importance over time. In this paper, reliability models are adapted to incorporate monitoring data on operating assets, as well as information on their environmental conditions, in their calculations. To that end, a logical decision tool based on two artificial neural networks models is presented. This tool allows updating assets reliability analysis according to changes in operational and/or environmental conditions. The proposed tool could easily be automated within a supervisory control and data acquisition system, where reference values and corresponding warnings and alarms could be now dynamically generated using the tool. Thanks to this capability, on-line diagnosis and/or potential asset degradation prediction can be certainly improved. Reliability models in the tool presented are developed according to the available amount of failure data and are used for early detection of degradation in energy production due to power inverter and solar trackers functional failures. Another capability of the tool presented in the paper is to assess the economic risk associated with the system under existing conditions and for a certain period of time. This information can then also be used to trigger preventive maintenance activities