Machine Learning Modeling of Horizontal Photovoltaics Using Weather and Location Data

Solar energy is a key renewable energy source; however, its intermittent nature and potential for use in distributed systems make power prediction an important aspect of grid integration. This research analyzed a variety of machine learning techniques to predict power output for horizontal solar panels using 14 months of data collected from 12 northern-hemisphere locations. We performed our data collection and analysis in the absence of irradiation data—an approach not commonly found in prior literature. Using latitude, month, hour, ambient temperature, pressure, humidity, wind speed, and cloud ceiling as independent variables, a distributed random forest regression algorithm modeled the combined dataset with an R2 value of 0.94. As a comparative measure, other machine learning algorithms resulted in R2 values of 0.50–0.94. Additionally, the data from each location was modeled separately with R2 values ranging from 0.91 to 0.97, indicating a range of consistency across all sites. Using an input variable permutation approach with the random forest algorithm, we found that the three most important variables for power prediction were ambient temperature, humidity, and cloud ceiling. The analysis showed that machine learning potentially allowed for accurate power prediction while avoiding the challenges associated with modeled irradiation data

In Situ Solar Panel Output Power Measurement Related to Climate Parameters using Digital Recording

Solar energy in tropical area is one of potential renewable energy due to the sun always shines along of a year involving in dry and rainy season. The power output of solar panel is depended on climate parameters including solar radiation, humidity, cloud, rain, and dust decomposition. This paper explores the climate parameters that potentially affect the power output of solar panel and estimate the solar energy in such region based on its climate parameters. To measure the climate and electric data, this work develops in house digital climate and electrical data recording that saves the data of humidity, wind speed, temperature, solar irradiance, current, and voltage sensors. The data are analyzed by equation of correlation between climate parameters, power output and solar panel temperature. The protoype of digital recording is tested at Politeknik Negeri Semarang located on latitude -7.054044 and longitude 110.434695 during dry season. Based on the correlation analysis of several climate parameters to solar panel output power, the correlation value of the humidity is -0.85, ambient temperature=0.87, solar irradiance=0.98, wind speed= -0.34 and cell temperature=0.83. This work can be used to estimate the potency of solar panel output power in specific location using climate data

Collinsville solar thermal project: yield forecasting (final report)

Executive Summary 1        Introduction This report’s primary aim is to provide yield projections for the proposed Linear Fresnel Reflector (LFR) technology plant at Collinsville, Queensland, Australia.  However, the techniques developed in this report to overcome inadequate datasets at Collinsville to produce the yield projections are of interest to a wider audience because inadequate datasets for renewable energy projects are commonplace.  Our subsequent report called ‘Energy economics and dispatch forecasting’ (Bell, Wild & Foster 2014a) uses the yield projections from this report to produce long-term wholesale market price and dispatch forecasts for the plant.  2        Literature review The literature review discusses the four drivers for yield for LFR technology: DNI (Direct Normal Irradiance) Temperature Humidity Pressure Collinsville lacks complete historical datasets of the four drivers to develop yield projections but its three nearby neighbours possess complete datasets, so could act as proxies for Collinsville.  However, analysing the four drivers for Collinsville and its three nearby sites shows that there is considerable difference in their climates.  This difference makes them unsuitable to act as proxies for yield calculations.  Therefore, the review investigates modelling the four drivers for Collinsville. We introduce the term “effective” DNI to help clarify and ameliorate concerns over the dust and dew effects on terrestrial DNI measurement and LFR technology. We also introduce a modified Typical Metrological Year (TMY) technique to overcome technology specific TMYs.  We discuss the effect of climate change and the El Niño Southern Oscillation (ENSO) on yield and their implications for a TMY. 2.1     Research questions Research questions arising from the literature review include: The overarching research question: Can modelling the weather with limited datasets produce greater yield predictive power than using the historically more complete datasets from nearby sites? This overarching question has a number of smaller supporting research questions: Does BoM adequately adjust its DNI satellite dataset for cloud cover at Collinsville? Given the dust and dew effects, is using raw satellite data sufficient to model yield? Does elevation between Collinsville and nearby sites affect yield? How does the ENSO cycle affect yield? Given the 2007-12 electricity demand data constraint, will the 2007-13 based TMY provide a “Typical” year over the ENSO cycle? How does climate change affect yield? Is the method to use raw satellite DNI data to calculate yield and retrospectively adjusting the calculated yield with an effective to satellite DNI energy per area ratio suitable? How has climate change affected the ENSO cycle? A further research question arises in the methodology but is included here for completeness. What is the expected frequency of oversupply from the Linear Fresnel Novatec Solar Boiler? 3        Methodology In the methodology section, we discuss the data preparation and the model selection process for the four drivers of yield.  We also discuss the development of the technology specific TMY and sensitivity analysis to address the research questions on climate change and elevation. 4        Results and analysis In the results section we present the selection process for the four driver models.  We also present the effective to satellite DNI ratio, the annual variation in gross yield, the selection of TMMs for the TMY based on monthly yield, the sensitivity analysis results on climate change and elevation, and the frequency of gross yield exceeding 30 MW. 5        Discussion We analyse the results within a wider context, in particular, we make a comparison with the yield calculations for Rockhampton to address the overarching research question.  We find that the modelling of weather at Collinsville using incomplete weather data has higher predictive performance that using the complete weather data at Rockhampton but recommend using the BoM’s one-minute solar data to improve the comparative test.  Other findings include the requirement to increase the current TMM’s selection period 2007-13 to incorporate more of the ENSO cycle.  There is less than 0.3% change in gross yield from the plant in the most likely case of climate change but there is a requirement to determine the effect of climate change on electricity demand and the ensuing change in wholesale electricity prices. 6        Conclusion In this report, we have addressed the key research questions, produced the yield projections for our subsequent report ‘Energy economics and dispatch forecasting’ (Bell, Wild & Foster 2014a) and made recommendations for further research

Earth-observation-based estimation and forecasting of particulate matter impact on solar energy in Egypt

This study estimates the impact of dust aerosols on surface solar radiation and solar energy in Egypt based on Earth Observation (EO) related techniques. For this purpose, we exploited the synergy of monthly mean and daily post processed satellite remote sensing observations from the MODerate resolution Imaging Spectroradiometer (MODIS), radiative transfer model (RTM) simulations utilizing machine learning, in conjunction with 1-day forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). As cloudy conditions in this region are rare, aerosols in particular dust, are the most common sources of solar irradiance attenuation, causing performance issues in the photovoltaic (PV) and concentrated solar power (CSP) plant installations. The proposed EO-based methodology is based on the solar energy nowcasting system (SENSE) that quantifies the impact of aerosol and dust on solar energy potential by using the aerosol optical depth (AOD) in terms of climatological values and day-to-day monitoring and forecasting variability from MODIS and CAMS, respectively. The forecast accuracy was evaluated at various locations in Egypt with substantial PV and CSP capacity installed and found to be within 5–12% of that obtained from the satellite observations, highlighting the ability to use such modelling approaches for solar energy management and planning (M&P). Particulate matter resulted in attenuation by up to 64–107 kWh/m2 for global horizontal irradiance (GHI) and 192–329 kWh/m2 for direct normal irradiance (DNI) annually. This energy reduction is climatologically distributed between 0.7% and 12.9% in GHI and 2.9% to 41% in DNI with the maximum values observed in spring following the frequent dust activity of Khamaseen. Under extreme dust conditions the AOD is able to exceed 3.5 resulting in daily energy losses of more than 4 kWh/m2 for a 10 MW system. Such reductions are able to cause financial losses that exceed the daily revenue values. This work aims to show EO capabilities and techniques to be incorporated and utilized in solar energy studies and applications in sun-privileged locations with permanent aerosol sources such as Egypt

Short-term forecasting photovoltaic solar power for home energy management systems

Accurate photovoltaic (PV) power forecasting is crucial to achieving massive PV integration in several areas, which is needed to successfully reduce or eliminate carbon dioxide from energy sources. This paper deals with short-term multi-step PV power forecasts used in model-based predictive control for home energy management systems. By employing radial basis function (RBFs) artificial neural networks (ANN), designed using a multi-objective genetic algorithm (MOGA) with data selected by an approximate convex-hull algorithm, it is shown that excellent forecasting results can be obtained. Two case studies are used: a special house located in the USA, and the other a typical residential house situated in the south of Portugal. In the latter case, one-step-ahead values for unscaled root mean square error (RMSE), mean relative error (MRE), normalized mean average error (NMAE), mean absolute percentage error (MAPE) and R2 of 0.16, 1.27%, 1.22%, 8% and 0.94 were obtained, respectively. These results compare very favorably with existing alternatives found in the literature.Programa Operacional Portugal 2020 and Operational Program CRESC Algarve 2020 grant 01/SAICT/2018. Antonio Ruano acknowledges the support of Fundação para a Ciência e Tecnologia, through IDMEC, under LAETA, grant UIDB/50022/2020.info:eu-repo/semantics/publishedVersio

Modeling Power Output of Horizontal Solar Panels Using Multivariate Linear Regression and Random Forest Machine Learning

United States Air Force energy resiliency goals are aimed to increase renewable energy implementation among its facilities. Researchers at the Air Force Institute of Technology designed, manufactured, and distributed 37 photovoltaic test systems to Air Force installations around the world. This research uses two types of modeling techniques, multivariate linear regression and random forest machine learning, to determine which technique will better predict power output for horizontal solar panels. Many previous solar panel prediction studies use solar irradiation data as an input. This study does not use irradiation as an input and aims to predict power output with input variables that are more readily available. If power output of a horizontal solar panel can be predicted using available weather data, then assessing the possibility of utilizing horizontal panels in any global location becomes possible. Input variables used for each model was latitude, month, hour, ambient temperature, humidity, wind speed, cloud ceiling, and altitude. The variance each model accounted was used as a comparison measure. The multivariate linear regression model accounted for 56.2% of the variance in a sample validation dataset. The random forest machine learning model accounted for 65.8% variance. The random forest model outperformed the multivariate linear regression model by accounting for 9.6% more variance. The most important variable in reducing the random forest model mean squared error was the month of the year, closely followed by cloud ceiling. Wind speed was the least important variable in reducing model error. More predictor variables are needed to increase predictability of horizontal solar panel power output if irradiation is not present as an input

A comprehensive study of diagnosis faults techniques occurring in photovoltaic generators

Recently, many focuses have been done in the field of renewable energies, especially in solar photovoltaic energy. Photovoltaic generator, considered as the heart of any photovoltaic installation, exhibits sometimes malfunctions which involve degradations on the overall photovoltaic plant. Therefore, diagnosis techniques are required to ensure failures detection. They avoid dangerous risks, prevent damages, allow protection, and extend their healthy life. For these purposes, many recent studies have given focuses on this field. This paper summarizes a large number of such interesting works. It presents a survey of photovoltaic generator degradations kinds, several types of faults, and their major diagnosis techniques. Comparative studies and some critical analyses are given. Other trending diagnosis solutions are also discussed. A proposed neural networks-based technique is developed to clarify the main process of diagnosis techniques, using artificial intelligence. This method shows good results for modelling and diagnosing the healthy and faulty (shaded) photovoltaic array

EQUIVALENT MODELS FOR PHOTOVOLTAIC CELL – A REVIEW

Over the years, the contribution of photovoltaic energy to an eco-friendly world is continually increasing. Photovoltaic (PV) cells are commonly modelled as circuits, so finding the appropriate circuit model parameters of PV cells is crucial for performance evaluation, control, efficiency computations and maximum power point tracking of solar PV systems. The problem of finding circuit model of solar PV cells is referred to as “PV cell equivalent model problem”. In this paper, the existing research works on PV cell model parameter estimation problem are classified according to error quali-quantitative analysis, number of parameters, translation equations and PV technology. The existent models were discussed pointing out its different levels of approximation. A qualitative comparative ranking was made and four models were found to be the best ones for simulating PV cells. Besides, based on the conducted review, some recommendations for future research are provided