Intelligent energy buildings based on RES and Nanotechnology

The paper presents the design features, the energy modelling and optical performance details of two pilot Intelligent Energy Buildings, (IEB). Both are evolution of the Zero Energy Building (ZEB) concept. RES innovations backed up by signal processing, simulation models and ICT tools were embedded into the building structures in order to implement a new predictive energy management concept. In addition, nano-coatings, produced by TiO2 and ITO nano-particles, were deposited on the IEB structural elements and especially on the window panes and the PV glass covers. They exhibited promising SSP values which lowered the cooling loads and increased the PV modules yield. Both pilot IEB units were equipped with an on-line dynamic hourly solar radiation prediction model, implemented by sensors and the related software to manage effectively the energy source, the loads and the storage or the backup system. The IEB energy sources covered the thermal loads via a south façade embedded in the wall and a solar roof which consists of a specially designed solar collector type, while a PV generator is part of the solar roof, like a compact BIPV in hybrid configuration to a small wind turbine

A PV temperature prediction model for BIPV configurations, comparison with other models and experimental results

The temperatures of c-Si and pc-Si BIPV configurations of different manufacturers were studied when operating under various environmental conditions. The BIPV configurations formed part of the roof in a Zero Energy Building, (ZEB), hanged over windows with varying inclination on a seasonal basis and finally two identical 0.5kWp PV generators were mounted on a terrace in two modes: fixed inclination and sun-tracking. The PV and ambient temperatures, Tpv and Ta, respectively, the intensity of the global solar radiation on the modules, IT, and the wind velocity on their surface, vw, were monitored for 2 years. The effect of the intensity, IT, the PV module inclination and vw, on Tpv was investigated. The values of the coefficient f relating Tpv and IT, were determined and argued for the configurations studied. A theoretical model was elaborated to predict Tpv and f for the cases of PV modules embedded on a roof, hanging over the windows and in free standing configurations. The effect of vw on f dominated for PV modules mounted on the terrace compared to the BIPV configurations in wind protected areas

A New Dynamic Model to Predict Transient and Steady State PV Temperatures Taking into Account the Environmental Conditions

Photovoltaic (PV) cell and module temperature profiles, Tc and Tpv, respectively, developed under solar irradiance were predicted and measured both at transient and steady state conditions. The predicted and measured Tc or Tpv covered both a bare c-Si PV cell, by SOLARTEC, at laboratory conditions using a solar light simulator, as well as various c-Si and pc-Si modules (SM55, Bioenergy 195W, Energy Solutions125W) operating in field conditions. The time constants, τ, of the Tc and Tpv profiles were determined by the proposed model and calculated using the experimentally obtained profiles for both the bare PV cell and PV modules. For model validation, the predicted steady state and transient temperature profiles were compared with experimental ones and also with those generated from other models. The effect of the ambient temperature, Ta, wind speed, vw, and the solar irradiance, IT, on the model performance, as well as of the mounting geometries, was investigated and incorporated in the prediction model. The predicted temperatures had the best matching to the measured ones in comparison to those from six other models. The model developed is applicable to any geographical site and environmental conditions

Dynamic Electro-Thermal PV Temperature and Power Output Prediction Model for Any PV Geometries in Free-Standing and BIPV Systems Operating under Any Environmental Conditions

PV temperature significantly affects the module’s power output and final system yield, and its accurate prediction can serve the forecasting of PV power output, smart grid operations, online PV diagnostics and dynamic predictive management of Building Integrated Photovoltaic (BIPV) systems. This paper presents a dynamic PV temperature prediction model based on transient Energy Balance Equations, incorporating theoretical expressions for all heat transfer processes, natural convection, forced convection, conduction and radiation exchanges between both module sides and the environment. The algorithmic approach predicts PV temperature at the centre of the cell, the back and the front glass cover with fast convergence and serves the PV power output prediction. The simulation model is robust, predicting PV temperature with high accuracy at any environmental conditions, PV inclination, orientation, wind speed and direction, and mounting configurations, free-standing and BIPV. These, alongside its theoretical basis, ensure the model’s wide applicability and clear advantage over existing PV temperature prediction models. The model is validated for a wide range of environmental conditions, PV geometries and mounting configurations with experimental data from a sun-tracking, a fixed angle PV and a BIPV system. The deviation between predicted and measured power output for the fixed-angle and the sun-tracking PV systems was estimated at −1.4% and 1.9%, respectively. The median of the temperature difference between predicted and measured values was as low as 0.5 °C for the sun-tracking system, and for all cases, the predicted temperature profiles were closely matching the measured profiles. The PV temperature and power output predicted by this model are compared to the results produced by other well-known PV temperature models, illustrating its high predictive capacity, accuracy and robustness

A spatiotemporal universal model for the prediction of the global solar radiation based on Fourier series and the site altitude

This paper presents the development, testing and validation of a novel generic type universal model consisting of a set of sine and cosine harmonics in the temporal and spatial domain suitably parameterized for the prediction of the mean expected global solar radiation H(n,φ) on the horizontal for a day, n, at any latitude φ. Its prediction power is further enhanced with the introduction of a correction term for the site altitude taking into account the φ dependent atmospheric height. Solar radiation data from 53 stations around the earth were obtained from GEBA database to train the model. H(n,φ) is expressed by a Fourier series of compact form with the zero frequency component dependent on φ providing the main spatial dependence and two n dependent harmonics in the form of cosine functions giving the time dependence. The φ dependent model parameters follow symmetry rules and are expressed by Fourier series up to the 3rd order harmonic. The 3D spatiotemporal profile of the model is in agreement to the extraterrestrial one. The model was validated using GEBA data from additional 28 sites and compared with NASA, PVGIS and SoDa data, showing the robustness, reliability and prediction accuracy of the proposed model

On a universal model for the prediction of the daily global solar radiation

A model to predict the mean expected daily global solar radiation, H(n) on a day n, at a site with latitude φ is proposed. The model is based on two cosine functions. A regression analysis taking into account the mean measured values Hm.meas(n) obtained from SoDa database for 42 sites in the Northern Hemisphere resulted in a set of mathematical expressions of split form to predict H(n). The parameters of the two cosine model for 0o<φ<23o are obtained by regression analysis using a sum of 3-8 Gaussian functions, while for 23o<φ<71o the two cosine model parameters are expressed by a sum of exponential functions or the product of an exponential and a cosine function. The main equation of the model and the set of parametric expressions provide H(n) for any φ on Earth. Validation results of this model are provided along with the statistical estimators NMBE, NRMSE and t-statistic in comparison to the corresponding values from three databases of NASA, SoDa and the measured values from ground stations provided in Meteonorm