A Repetitive Day Method for Predicting the Long-Term Thermal Performance of Passive Solar Buildings

1990 ◽  
Vol 112 (1) ◽  
pp. 34-42 ◽  
Author(s):  
D. Feuermann
Keyword(s):  
Solar Radiation ◽  
Ambient Temperature ◽  
Thermal Performance ◽  
Meteorological Data ◽  
Climatic Data ◽  
Temperature Model ◽  
Governing Equations ◽  
Model Calculations ◽  
Passive Solar

The long-term thermal performance of passively-heated solar buildings is predicted by a single repetitive meteorological day which contains judiciously chosen solar radiation and ambient temperature functions. These are used as the driving functions of the governing equations that describe the passive solar building under study. The solar radiation and ambient temperature functions are chosen such that they include, both qualitatively and quantitatively, the essential radiation and temperature statistics of the climate in which the building is to be located. The relevant statistics are determined from hourly meteorological data. When hourly meteorological data are not available for a given location, the solar radiation and ambient temperature functions can be constructed from the knowledge of only two climatic data, namely, the monthly average horizontal radiation and the ambient temperature. Model calculations compare favorably with experimental data from Los Alamos solar test cells and with computer simulations.

Artificial Intelligence Techniques for Solar Energy and Photovoltaic Applications

2013 ◽  
pp. 376-436 ◽  
Author(s):  
Radian Belu
Keyword(s):  
Artificial Intelligence ◽  
Solar Radiation ◽  
Solar Energy ◽  
Meteorological Data ◽  
Energy Systems ◽  
Poor Quality ◽  
Quality Of Data ◽  
Information Patterns ◽  
Time Requirements

Artificial intelligence (AI) techniques play an important role in modeling, analysis, and prediction of the performance and control of renewable energy. The algorithms employed to model, control, or to predict performances of the energy systems are complicated involving differential equations, large computer power, and time requirements. Instead of complex rules and mathematical routines, AI techniques are able to learn the key information patterns within a multidimensional information domain. Design, control, and operation of solar energy systems require long-term series of meteorological data such as solar radiation, temperature, or wind data. Such long-term measurements are often non-existent for most of the interest locations or, wherever they are available, they suffer of a number of shortcomings (e.g. poor quality of data, insufficient long series, etc.). To overcome these problems AI techniques appear to be one of the strongest candidates. The chapter provides an overview of commonly used AI methodologies in solar energy, with a special emphasis on neural networks, fuzzy logic, and genetic algorithms. Selected AI applications to solar energy are outlined in this chapter. In particular, methods using the AI approach for the following applications are discussed: prediction and modeling of solar radiation, seizing, performances, and controls of the solar photovoltaic (PV) systems.

Using Modified Sandia Method in Developing Typical Solar Radiation Year for Photovoltaic Electricity Generation Projection

2011 ◽  
Vol 71-78 ◽  
pp. 4374-4381 ◽  
Author(s):  
Kuo Tsang Huang ◽  
Wen Sheng Ou
Keyword(s):  
Solar Radiation ◽  
Electricity Generation ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Climate Variations ◽  
Surface Solar Radiation ◽  
Estimation Process ◽  
Pv Panel ◽  
Southern Taiwan

The energy generation efficiency of Building Intergraded Photovoltaic Systems (BIPV) system relies much on the panel’s surface solar radiation received. In the projection of annual power generation of photovoltaic panels, local global solar radiation plays a pivotal role for reliable estimation process. The purpose of this paper is to develop an hourly typical solar radiation year (TSRY) as fundamental meteorological database for utilizing the estimation process. The TSRY should be interpretable to local long-term climate variations, thus, ten years' hourly meteorological data were gathered to formulate a typical year by means of modified Sandia method herein. A total of four cities' hourly typical years from northern to southern Taiwan were established in this paper. Orientation and inclination effect of the PV panel were also discussed in terms of daily averaged global solar radiation that cumulate from TSRY.

scholarly journals Forecasting the Carsharing Service Demand Using Uni and Multivariable Models

2020 ◽  
Author(s):  
Victor Aquiles Alencar ◽  
Lucas Ribeiro Pessamilio ◽  
Felipe Rooke da Silva ◽  
Heder Soares Bernardino ◽  
Alex Borges Vieira
Keyword(s):  
Short Term Memory ◽  
Meteorological Data ◽  
Absolute Error ◽  
Climatic Data ◽  
Short Term ◽  
Car Sharing ◽  
Urban Centers ◽  
Service Demand ◽  
Boosting Algorithms

Abstract Car-sharing is an alternative to urban mobility that has been widely adopted. However, this approach is prone to several problems, such as fleet imbalance, due to the variance of the daily demand in large urban centers. In this work, we apply two time series techniques, namely, Long Short-Term Memory (LSTM) and Prophet, to infer the demand for three real car-sharing services. We also apply several state-of-the-art models on free-floating data in order to get a better understanding of what works best for this type of data. In addition to historical data, we also use climatic attributes in LSTM applications. As a result, the addition of meteorological data improved the model’s performance, especially on Evo: an average Mean Absolute Error (MAE) of approximately 61.13 travels was obtained with the demand data on Evo, while MAE equals 32.72 travels was observed when adding the climatic data, the other datasets also improved but none other improved this much. For the free-floating data test, we got the Boosting Algorithms (XGBoost, Catboost, and LightGBM) got the best performance short term, the worst one has an improvement of around 22% of MAE over the next best-ranked (Prophet). Meanwhile in the long term Prophet got the best MAE result, around 22.5% better than the second-best (LSTM).

scholarly journals Evaluation of the PV energy production determined by measurements, simulation and analytical calculations

2019 ◽  
Vol 85 ◽  
pp. 04002
Author(s):  
Dorin Petreuş ◽  
Mugur Bălan ◽  
Octavian Pop ◽  
Radu Etz ◽  
Toma Patărău
Keyword(s):  
Solar Radiation ◽  
Ambient Temperature ◽  
Analytical Model ◽  
Energy Production ◽  
Meteorological Data ◽  
Experimental System ◽  
Absolute Error ◽  
Simulation Software ◽  
Accuracy Evaluation ◽  
Tilted Plane

The study provides a comparative analysis of the energy production of a 3 kW peak PV array connected in an islanded microgrid, in correlation with solar radiation and ambient temperature measurements. The experimental system is located in Cluj-Napoca Romania and was monitored during the year 2017, based on a graphical user interface. It was also evaluated the capability to predict the PV energy production by using the PV*SOL simulation software and an analytical model, developed at the Technical University of Cluj-Napoca. As input data in the analytical model was used the measured solar radiation and ambient temperature while in the simulations was used alternatively measured data and average meteorological data available in the software database. Besides energy production it was compared the solar radiation on the tilted plane of the PV panels, the PV panel's temperature and the system efficiency. For the predictions accuracy evaluation it was used the weighted mean absolute error based on total energy production, which was found to be lower than 1%, in good agreement with the values reported in literature. The outcomes of this study are valuable for expanding the PV installations in this area and for predictive energy management developments.

scholarly journals Historical and Future Typical Meteorological Years for 33 locations in Greece: a handy tool for various applications

2021 ◽  
Author(s):  
Basil Psiloglou ◽  
Harry D. Kambezidis ◽  
Konstantinos V. Varotsos ◽  
Dimitris G. Kaskaoutis ◽  
Dimiitris Karagiannis ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Design Tool ◽  
Weather Data ◽  
Climate Simulation ◽  
Total Solar Radiation ◽  
Climatic Data ◽  
Data Set ◽  
Specific Location ◽  
Daily Data

<p>It is generally accepted that a climatic data set of meteorological measurements with true sequences and real interdependencies between meteorological variables is needed for a representative climate simulation. In the late 1970s the Typical Meteorological Year (TMY) concept was introduced in USA as a design tool for approximating expected climate conditions at specific locations, at a time when computers were much slower and had less memory than today. A TMY is a collation of selected weather data for a specific location, listing usually hourly values of meteorological and solar radiation elements for one-year period. The values are generated from a data bank much longer than a year in duration, at least 10 years. It is specially selected so that it presents the range of weather phenomena for the location in question, while still giving annual averages that are consistent with the long-term averages for the specific location. Each TMY data file consists of 12 months chosen as most “typical“ among the years present in the long-term data set. Although TMYs do not provide information about extreme events and do not necessarily represent actual conditions at any given time, they still reflect all the climatic information of the location. TMY sets remain in popular use until today providing a relatively concise data set from which system performance estimates can be developed, without the need of incorporating large amounts of data into simulation models. </p><p>TMY sets for 33 locations in Greece distributed all over the country were developed, covering for the first time all climatic zones, for both historical and future periods. Historical TMY sets generation was based on meteorological data collected from the Hellenic National Meteorological Service (HNMS) network in Greece in the period 1985-2014, while the corresponding total solar radiation values have been derived through the Meteorological Radiation Model (MRM).</p><p>Moreover, the generation of future TMY sets for Greece was also performed, for all 33 locations. To this aim, bias adjusted daily data for the closest grid point to the HNMS station’s location were employed from the RCA4 Regional Climate Model of the Swedish Meteorological and Hydrological Institute (SMHI) driven by the Earth system model of the Max Planck Institute for Meteorology (MPI-M). Simulations were carried out in the framework of the EURO-CORDEX modeling experiment, with a horizontal RCA4 model resolution of 0.11<sup>o</sup> (~12 x 12 km). We used daily data for four periods: the 1985-2014 used as reference period and the 2021-2050, 2046-2070 and 2071-2100 future periods under RCP4.5 and RCP8.5 scenarios. </p><p>This work was carried out in the framework of the “Development of synergistic and integrated methods and tools for monitoring, management and forecasting of environmental parameters and pressures” (KRIPIS-THESPIA-II) Greek national funded project.</p>

Estimating hourly incoming solar radiation from limited meteorological data

Weed Science ◽  
2006 ◽  
Vol 54 (1) ◽  
pp. 182-189 ◽  
Author(s):  
Kurt Spokas ◽  
Frank Forcella
Keyword(s):  
Solar Radiation ◽  
Growth Models ◽  
Meteorological Data ◽  
Pearson Correlation ◽  
Absolute Error ◽  
Incident Radiation ◽  
Climatic Data ◽  
Agricultural Field ◽  
Radiation Beam ◽  
Soil System

Two major properties that determine weed seed germination are soil temperature and moisture content. Incident radiation is the primary variable controlling energy input to the soil system and thereby influences both moisture and temperature profiles. However, many agricultural field sites lack proper instrumentation to measure solar radiation directly. To overcome this shortcoming, an empirical model was developed to estimate total incident solar radiation (beam and diffuse) with hourly time steps. Input parameters for the model are latitude, longitude, and elevation of the field site, along with daily precipitation with daily minimum and maximum air temperatures. Field validation of this model was conducted at a total of 18 sites, where sufficient meteorological data were available for validation, allowing a total of 42 individual yearly comparisons. The model performed well, with an average Pearson correlation of 0.92, modeling index of 0.95, modeling efficiency of 0.80, root mean square error of 111 W m−2, and a mean absolute error of 56 W m−2. These results compare favorably to other developed empirical solar radiation models but with the advantage of predicting hourly solar radiation for the entire year based on limited climatic data and no site-specific calibration requirement. This solar radiation prediction tool can be integrated into dormancy, germination, and growth models to improve microclimate-based simulation of development of weeds and other plants.

scholarly journals Artificial Intelligence Techniques for Solar Energy and Photovoltaic Applications

Robotics ◽  
2013 ◽  
pp. 1662-1720 ◽  
Author(s):  
Radian Belu
Keyword(s):  
Artificial Intelligence ◽  
Solar Radiation ◽  
Solar Energy ◽  
Meteorological Data ◽  
Energy Systems ◽  
Poor Quality ◽  
Quality Of Data ◽  
Information Patterns ◽  
Time Requirements

Artificial intelligence (AI) techniques play an important role in modeling, analysis, and prediction of the performance and control of renewable energy. The algorithms employed to model, control, or to predict performances of the energy systems are complicated involving differential equations, large computer power, and time requirements. Instead of complex rules and mathematical routines, AI techniques are able to learn the key information patterns within a multidimensional information domain. Design, control, and operation of solar energy systems require long-term series of meteorological data such as solar radiation, temperature, or wind data. Such long-term measurements are often non-existent for most of the interest locations or, wherever they are available, they suffer of a number of shortcomings (e.g. poor quality of data, insufficient long series, etc.). To overcome these problems AI techniques appear to be one of the strongest candidates. The chapter provides an overview of commonly used AI methodologies in solar energy, with a special emphasis on neural networks, fuzzy logic, and genetic algorithms. Selected AI applications to solar energy are outlined in this chapter. In particular, methods using the AI approach for the following applications are discussed: prediction and modeling of solar radiation, seizing, performances, and controls of the solar photovoltaic (PV) systems.

The geographical distribution of the Queensland fruit fly, Bactrocera (Dacus) tryoni, in relation to climate

1998 ◽  
Vol 49 (6) ◽  
pp. 935 ◽  
Author(s):  
Robert W. Sutherst ◽  
Tania Yonow
Keyword(s):  
Cold Stress ◽  
Geographical Distribution ◽  
Meteorological Data ◽  
Agricultural Practices ◽  
Fruit Fly ◽  
Threshold Temperature ◽  
Temperature Model ◽  
Developmental Threshold ◽  
Stress Accumulation

CLIMEX is used to analyse the potential distribution of the Queensland fruit fly in relation to long-term average meteorological data. Different hypotheses on the mechanisms limiting the distribution of this species are examined. The analyses indicate that different CLIMEX models discriminate between locations in different ways. In particular, the models describing the limiting effects of cold stress yield substantially different estimates of the areas that can support overwintering populations. With the threshold temperature model of cold stress, extreme low temperatures exclude flies from high-altitude areas, but fail to exclude them from areas known not to support overwintering populations. These areas can only be rendered unfavourable by using the degree-day model of cold stress, which prevents sufficient thermal accumulation above the developmental threshold to maintain basic metabolic processes for long periods. In contrast, 2 models describing different modes of heat stress accumulation provide similar results and are interchangeable. Our analyses also indicate the potential for agricultural practices, such as irrigation, to alter quite dramatically the suitability of an area for Queensland fruit fly, and impact upon its geographical distribution and the pattern of activity.

Crop continuity and prediction of maturity in the crisp lettuce variety Saladin

1988 ◽  
Vol 111 (3) ◽  
pp. 481-486 ◽  
Author(s):  
D. C. E. Wurr ◽  
Jane R. Fellows ◽  
R. F. Suckling
Keyword(s):  
Solar Radiation ◽  
Linear Relationship ◽  
Soil Type ◽  
Meteorological Data ◽  
Accurate Prediction ◽  
Site Factor ◽  
Commercial Crop ◽  
A Site ◽  
Crop Maturity

SummaryThe dates of transplanting and maturity of twenty-four experiments between 1980 and 1986 with the lettuce varieties Saladin and Saladin R 100 were used, together with meteorological data for each crop, to study the planning of crop continuity and the prediction of crop maturity. The interval in days from transplanting to maturity was shown to be a minimum for crops transplanted around the middle of June and was described by a fitted quadratic relationship which accounted for 34·6% of the variance. When expressed in accumulated day-degrees >0 °C the interval increased with later transplanting but a linear relationship accounted for only 22·2% of the variance. However, when expressed as accumulated solar radiation the interval decreased with later transplanting and a linear relationship accounted for 82·2% of the variance. When expressed in effective day-degrees (EDDs) the interval was relatively stable at 589 EDDs (S.D. = 39·5).Predictions of commercial crop maturity based on these relationships and using a combination of average and observed meteorological data showed that EDDs gave the most accurate prediction. Differences between actual and predicted maturity appeared to be associated with the site of production, suggesting that a ‘site’ factor needs to be incorporated to take account of local differences in temperature, solar radiation and soil type. The use of long-term average day-degree and EDD data was less consistent than estimates of maturity based wholly or predominantly on observed day-degree and EDD data though with solar radiation the reverse was true.

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