Data-Driven Model for Solar Panel System Performance Decision Making: Longitudinal Assessment of Dust Accumulation and Panel Efficiency Degradation

doi:10.21203/rs.3.rs-4350908/v1

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Data-Driven Model for Solar Panel System Performance Decision Making: Longitudinal Assessment of Dust Accumulation and Panel Efficiency Degradation

https://doi.org/10.21203/rs.3.rs-4350908/v1

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The deployment of solar panels plays a vital role in generating clean energy and fulfilling countries' targets for carbon reduction. However, to sustain the energy output of solar panels, regular monitoring and maintenance are crucial. This is particularly significant in countries potentially adverse environments, such as Qatar, where dust poses a significant challenge to solar panel performance, leading to energy degradation. In response to this challenge, this paper proposes a data-driven model that facilitates cost-effective monitoring of homes solar panels and enables decision-makers to make informed decisions by leveraging Generated to Consumed Electrical Energy Ratio data. The model aims to address the impact of dust accumulation on solar panels and support the efficient management of solar panel system,

Data-driven model

Dust accumulation

Efficiency degradation

Generated to consumed electrical energy ratio

Renewables

Solar PV

1.1. Data-Driven Decision Making (DDDM)

Data plays a crucial role in decision making, providing valuable insights and evidence to support informed choices. DDDM is the process of using objective data to inform business decisions, rather than relying on intuition or personal judgment. This approach has become increasingly important in modern business environments, where vast amounts of data are available from various sources such as social media, customer data, and internal and industrial reports [1, 2]. Research has shown that DDDM leads to more accurate and effective decision making outcomes [3]. By collecting and analyzing relevant data, decision makers gain a deeper understanding of the underlying patterns, trends, and relationships within their domain, and such empirical evidence enables them to make better informed decisions that are grounded in objective information, rather than subjective opinions and intuitions subject to biases, minimizing the risk of erroneous judgments and suboptimal outcomes [4].

As illustrated in Fig. 1, the benefits of DDDM include valuable insights, continual growth, improved program outcomes, optimized operations, prediction of future trends, and actionable insights [5]. One of the key advantages of DDDM is its ability to uncover patterns and trends that may not be immediately apparent through other means. Data analysis enables the identification of correlations between different segments, allowing for tailored strategies for different groups [6]. Additionally, DDDM helps organizations mitigate the risk of errors or bias that can arise when decisions are based solely on subjective judgment. By relying on objective data, businesses can minimize the potential for decision-making errors and ensure that they make the best choices possible with the information at hand [7]. Hence, decision makers across various fields and industries recognize the significance of relying on data-driven approaches to enhance their decision-making processes [3].

1.2. DDDM in the Renewable Energy Industry

DDDM in the renewable energy industry has been widely recognized for its significant role in improving renewable energy planning, optimization, and management [8]. By utilizing data and advanced analytics, decision makers can gain valuable insights to support informed choices and drive the transition towards a sustainable and clean energy future. Renewable energy industry stakeholders (among others) have been implementing Data-Driven Decision Support Systems (DDDSS) to enhance decision-making processes, by harnessing large amounts of data and advanced analytical tools. In the context of the renewable energy industry, the following key elements are vital for the effectiveness of DDDSS [1]:

Data collection: Relevant data must be gathered from various sources specific to the industry, including internal databases, external sources such as renewable energy generation data, environmental factors, market trends, and customer data. Ensuring the quality of collected data is crucial to obtain accurate and reliable insights.
Data storage: Once collected, industrial data needs to be stored in a manner that is easily accessible and facilitates quick analysis. This may involve storing data in specialized databases or cloud-based storage solutions designed for renewable energy applications.
Data analysis: Advanced analytical tools and techniques are employed to analyze the data specific to the industry. This includes using machine learning algorithms, statistical analysis, and energy modeling techniques to uncover patterns, trends, and insights relevant to renewable energy generation, optimization, and forecasting.
Data visualization: Results of data analysis are presented in a visually intuitive manner to facilitate easy understanding and interpretation. This includes interactive dashboards, charts, maps, and other visualizations that help stakeholders grasp the complex relationships and make informed decisions regarding renewable energy projects and investments.

One key aspect where DDDM is valuable in the renewable energy sector is resource assessment and site selection. Through the analysis of historical weather data, topographical information, and other relevant data sources, decision makers can identify optimal locations for renewable energy projects such as wind farms or solar installations [9]. By considering factors such as wind speed, solar radiation, and land suitability, data-driven models (DDMs) can help maximize the energy generation potential and improve the overall efficiency of renewable energy systems. Furthermore, data-driven approaches can aid in the prediction and forecasting of renewable energy generation. By utilizing real-time monitoring data, weather forecasts, and predictive modeling techniques, decision makers can estimate the expected output from renewable energy sources with greater accuracy [10]. These forecasts enable effective grid integration, better resource allocation, and improved energy management, ultimately optimizing the utilization of renewable energy resources.

DDDM also plays a critical role in monitoring and maintenance of renewable energy infrastructure [10]. By collecting data from sensors, IoT devices, and remote monitoring systems, decision makers can continuously monitor the performance and health of renewable energy systems [11, 12]. This proactive approach allows for early detection of issues, timely maintenance, and improved operational efficiency, ensuring the optimal functioning of renewable energy assets [13]. Therefore, DDDM is instrumental in the renewable energy sector, facilitating resource assessment, forecasting, and monitoring of renewable energy systems. By leveraging data and advanced analytics, decision makers can optimize the deployment and management of renewable energy resources, contributing to a more sustainable and environmentally friendly energy landscape [9].

1.3. Data Driven Model (DDM) for Solar Panel Systems

Regular monitoring, maintenance, and upgrades are crucial for the long-term sustainability and efficiency of solar photovoltaic (PV) systems [14]. Advanced monitoring technologies, such as IoT and Intelligent Wireless Sensor Networks, are utilized in large solar panel plants for performance evaluation [15]. However, these technologies are too costly for smaller and localized private applications. Therefore, there is a need for a practical and affordable approach like Generated to Consumed Electrical Energy Ratio (GtoC) for homes and businesses with a limited number of solar panels [16]. Additionally, having enough data is essential for making the right decisions and establishing strategies. For instance, GtoC can be used as an indicator to support decision-making when the government desires to establish national renewable energy policies, to incentivize teams and consumers to install solar panel systems or support solar energy generation.

The implementation of GtoC within a DDDSS can assist various stakeholders in developing effective strategies and making informed decisions [17]. Moreover, GtoC can be used as an indicator to compare the current reading with the previous reading, when the system was at its peak performance, to support planning, cleaning, maintenance, or replacement of parts or whole systems [16]. This is crucial as dust, dirt, and ageing can reduce the performance of PV systems, particularly in Qatar, where high dust density occurs during specific months of the year [18]. For example, if the GtoC changes, it could indicate a decline in panel performance, which can be restored by performing cleaning to restore the normal GtoC. Figure 2 illustrates how dust can impact GtoC data.

2.1. Methods

As the objective is to introduce a data-driven system as a new mechanism to improve decision-making for solar roof panels, the Plan–Do–Study–Act (PDSA) methodology was adopted to establish the DDM to serve the purpose of this research project. PDSA has been widely used within healthcare and other industries to improve processes and achieve desired outcomes (Fig. 3) [19, 20]. The PDSA cycle is a four-step iterative process that involves planning, executing, evaluating, and refining a change [20]. In the initial Plan phase, the problem is identified, and a plan is developed to address it. In the Do phase, the plan is implemented, and data is collected. The Study phase involves analyzing the data collected in the previous step, to determine if the plan was successful in addressing the identified problem. Finally, in the Act phase, the findings from the Study phase are used to refine and improve the plan, which is then implemented again in the next PDSA cycle.

Adopting PDSA as a methodology for establishing a DDDSS for solar panel roofs can be effective because it allows for continuous improvement of the system over time. The Plan phase can involve identifying areas where data can be collected, and developing a plan for collecting and analyzing that data. In the Do phase, the plan can be implemented, and data can be collected from GtoC. In the Study phase, the data can be analyzed to identify patterns or trends, and to determine if the system is meeting its intended goals. Finally, in the Act phase, the findings can be used to refine the system and improve its performance in subsequent PDSA cycles.

As can be seen in Fig. 4, the process of creating a DDM involves several key stages [21], as attested by previous research [5]. It begins by defining the mission or problem at hand and then proceeds to identify the relevant data sources. Once the data is collected, it undergoes a thorough cleaning and organizing process, to ensure its quality and suitability for analysis [22]. The next stage involves performing statistical analyses on the cleaned and organized data, utilizing appropriate methods and techniques [23, 24]. This step aims to extract meaningful insights, identify patterns, and derive conclusions based on the information derived from the data and statistical analysis [24].

2.2. Proposed DDM

Countries like Qatar are prone to sandstorms, which can lead to the accumulation of sand on solar panels and degrade the amount of energy generated [25]. The proposed DDM, as depicted in Fig. 5, aims to contribute to the monitoring of changes in the performance of solar panel systems. By comparing the amount of GtoC under ideal working conditions with the amount of GtoC after dust accumulation, statistical analysis can determine if the impact of dust significantly degrades the amount of energy generated. This analysis can be conducted for a single home, a specific region with multiple homes, homes distributed across a city, or the total number of homes in a country. Based on these findings, informed decisions can be made to address the impact of dust accumulation on solar panel performance.

2.3. Identifying Mission/ Problem

Solar panels may experience degradation in the amount of generated energy due to aging, lack of maintenance, and other reasons [26]. It is important to monitor the extent of this degradation in order to determine the necessary course of action. With multiple solar panels installed on residential roofs, a reactive approach is often taken, waiting until the system completely stops or experiences a significant drop in energy generation before conducting checks. However, a proactive approach is more advantageous for sustaining clean energy generation [27]. Therefore, the objective of this study is to establish a mechanism that can effectively identify degradation in the amount of energy generated by home solar panels, providing stakeholders with valuable information to make informed decisions.

2.4. Identifying Data Sources

Identifying the right data sources is a critical factor in enabling the proposed approach to function as intended. In the related research conducted, it was found that the GtoC indicator can serve as a simple and useful data source [5]. This indicator can be extracted from a single home, a specific region, or even the entire country, depending on the analyses needs to be made. By utilizing this data, valuable insights into energy generation and consumption patterns, which can inform decision-making processes by different stakeholders and support the development of sustainable energy strategies. As previously established through an analytical study, GtoC data provides a wealth of information that can be used to supply DDMs with the necessary data to make informed decisions. GtoC data can be generated based on the following equations derived from previous research [16].

Estimated Daily Solar Panels Energy Generation Formula

$${G=\frac{\left({p}_{s}1000 {p}_{n}{ p}_{e }{s}_{PD }\right)}{1000} }_{ }$$

where $G$ is the total generated electrical energy from solar panels (kWh), ${p}_{s}$ is the solar panel area (m²), ${p}_{n}$ is the number of solar panels, ${p}_{e}$ is solar efficiency, and ${s}_{PD}$ is sun hours per day.

Generated to Consumed Electrical Energy Ratio (GtoC) Formula

$${G}_{to}C=\left(\frac{G}{C}\right)\times 100\%$$

where $G$is the total generated electrical energy from solar panels in KWh, and $C$is the consumed electrical energy by the house in KWh.

Average ${G}_{to}C$ for Number of Homes in City or District Formula

$${\left({G}_{to}C \right)}_{A }=\frac{\left(\sum _{i=1}^{n}{\left({G}_{to}C \right)}_{1 }+{\left({G}_{to}C \right)}_{2}+\dots +{\left({G}_{to}C \right)}_{n}\right)}{n}$$

where n is the total number of houses in the city or district.

National ${G}_{to}C$ Formula

$${\left({G}_{to}C \right)}_{N }=\frac{\left(\sum _{i=1}^{n}{\left({G}_{to}C \right)}_{A1 }+{\left({G}_{to}C \right)}_{A2}+\dots +{\left({G}_{to}C \right)}_{An}\right)}{n}$$

where n is the total number of cities or districts in country.

2.5. Cleaning and Organizing Data

Cleaning and organizing the data in DDMs is crucial to ensure accurate and reliable results. It is important to note that only 20% of the data will be used for analysis in order to make informed decisions; additionally, organizing the flow of the data is essential to ensure a meaningful approach that captures relevant information [28]. In this research, the implementation of the tree-topology approach (as shown in Fig. 6) is considered the most appropriate, as it enables a better flow and collection of data for subsequent statistical analysis. The tree-topology approach offers several advantages, including improved data handling, efficient data processing, and enhanced data integrity [28]. By utilizing this approach, the collected data can be managed and analyzed, effectively, leading to more accurate and reliable research outcomes.

Figure 6 illustrates the tree topology, which facilitates the collection of GtoC data across three tiers: home, city, and national levels. This hierarchical structure allows for statistical analysis to be conducted at different levels, enabling informed decision-making based on the data obtained from each tier. In order to establish a DDM to support decision-making regarding the impact of dust accumulation on solar panel performance and energy generation, an assumption was made based on a study of 100 homes. The GtoC data was calculated using information from a previous study, representing the ideal operational scenario. Subsequently, the data was degraded at different levels, including − 5%, -10%, and − 20%, as shown in the Table 1. This degraded data was then utilized to feed the model and perform the subsequent stage of statistical analysis, transforming the data into meaningful information that can be understood by various stakeholders.

Table 1

Utilized GtoC data
Month	Ideal situation	Degradation by [-5%]	Degradation by [-10%]	Degradation by [-15%]	Degradation by [-20%]
Jan	39.8208	37.82976	35.83872	33.84768	31.85664
Feb	42.5856	40.45632	38.32704	36.19776	34.06848
Mar	44.2368	42.02496	39.81312	37.60128	35.38944
Apr	47.6928	45.30816	42.92352	40.53888	38.15424
May	50.5728	48.04416	45.51552	42.98688	40.45824
Jun	51.2256	48.66432	46.10304	43.54176	40.98048
Jul	50.9568	48.40896	45.86112	43.31328	40.76544
Aug	48.2304	45.81888	43.40736	40.99584	38.58432
Sep	46.464	44.1408	41.8176	39.4944	37.1712
Oct	43.2	41.04	38.88	36.72	34.56
Nov	39.936	37.9392	35.9424	33.9456	31.9488
Dec	39.0528	37.10016	35.14752	33.19488	31.24224
Av. GtoC	45.3312	43.06464	40.79808	38.53152	36.26496

3.1. Descriptive Analysis

Descriptive analysis allows for the representation of data and provides a general overview of its behavior. This analysis, such as calculating means, is extracted from the data presented in Table 1. The radar plot in Fig. 7 illustrates the different degradation scenarios, enabling a comparison of each degradation level with the ideal scenario. Additionally, comparing the GtoC average for each degradation case with the average of the ideal scenario, as depicted in the Fig. 8, offers another method of assessing the different levels of degradation. However, it is important to note that descriptive analysis alone does not provide a meaningful comparison to determine if the changes are significantly different from the ideal scenario. To contextualize these findings and gain further insights, a comparative analysis should be incorporated. By combining descriptive and comparative analyses, the situational implications of the data can be better understood [29].

3.2. Comparative Analysis

Comparative analysis is a valuable approach for making meaningful comparisons between the degradation situation and the ideal situation. Since the comparison involves two distinct groups, conducting statistical tests becomes essential. One well-known test for such comparisons is the t-test, which examines the means of each group and determines if there is a significant difference. In cases where the level of degradation is high, the statistical test will reveal a significant difference [29]. Table 2 displays the results of various tests conducted to compare the different levels of degradation with the ideal situation. These tests involve calculating the t-value and p-value to establish the level of significance. The t-value reflects the extent of degradation, with higher values indicating greater degradation. The p-value is crucial for determining whether the observed degradation is statistically significant or not. Table 2 shows that the higher the t-value, the greater the level of degradation, while the p-value indicates the significance of the observed degradation.

Table 2

T-test statistical analysis summary
Degradation	Calculated t value	Calculated p value	Result significant [yes/ no]
-5%	1.26911	0.108832	No
-10%	2.60228	0.008132	Yes
-15%	4.00133.	0.000301	Yes
-20%	4.16806.	0.000289	Yes

3.3. Conclusion for Action

Figure 9 illustrates the relationship between the t value and degradation provides decision-makers with a visual representation that facilitates the understanding of the statistical findings presented earlier. The graph includes a horizontal line marked at 0%, representing the ideal situation. When there is minimal degradation, as seen in the case of a 5% degradation, the line closely aligns with the horizontal line. However, as the value of t increases with greater degradation, the line deviates towards the vertical axis. By observing these changes, decision-makers can determine the appropriate course of action. For example, when the degradation is at 5%, it can be concluded that the situation falls within the normal range. In cases where the degradation reaches 10%, it is recommended to implement a plan for solar panel cleaning. When the degradation exceeds 15%, it becomes crucial to conduct cleaning to address the significant degradation. The graphical representation in Fig. 9 of the t value against degradation offers decision-makers an intuitive way to interpret the data and make informed decisions based on the observed changes in the line’s position.

3.4. Integrating panels efficiency degradation over time

Model integration of panels’ efficiency degradation due to age is a critical consideration in comprehensive modeling for solar energy systems. PV panels experience a normative decrease in efficiency over time, which directly undermines the overall energy output of the system. To accurately depict the long-term performance of such systems, it is imperative to incorporate this degradation factor into the modeling process. In this context, it is observed that the panel efficiency diminishes progressively over the years. The established model incorporates a degradation rate of 0.2 for the initial year, followed by a subsequent decrease of 0.7 percent in each subsequent year [30]. This degradation pattern significantly influences the amount of energy generated by the system. By integrating this dynamic efficiency degradation into the model, a more holistic understanding of the system's behavior emerges, allowing for informed decision-making processes, particularly in matters concerning system upgrades or maintenance [31].

To illustrate the application of this degradation model, Fig. 10 depicts a flowchart exemplifying the programming function used for the calculation. Furthermore, Fig. 11 offers a 3D visual representation based on the data sourced from Table 1. The provided flowchart serves as a comprehensive guide, detailing the step-by-step process for calculating the efficiency for each year, thereby enhancing the clarity surrounding the evolution of efficiency over time. While the degradation pattern may not strictly adhere to a linear trajectory, Fig. 11 encapsulates the fundamental concept of efficiency reduction in panels for each successive year. Additionally, it affords valuable insights into the efficiency fluctuations throughout each month, extrapolated from the initial energy generation data established during the system’s setup phase. It is noteworthy that this dataset signifies the system’s peak performance during its initial installation [32].

The adaptation of continuous improvement approaches for effective decision-making within solar panel systems is essential to sustain the generation of clean energy and minimize the reliance on non-clean sources of energy. Failing to do so can have consequences, including increased costs and a failure to meet national sustainability targets. In this case, the use of a DDM proves to be effective in supporting decision-makers in monitoring solar panel degradation caused by dust accumulation. By leveraging G2C data as a valuable source of information, decision-makers can gain insights through descriptive analysis and complement it with comparative analysis, as demonstrated in the examples presented. This approach equips decision-makers with comprehensive information to make informed decisions and take necessary actions.

Moreover, the integration of panels efficiency degradation due to age in modeling solar energy systems is paramount for a comprehensive and accurate representation of their long-term performance. By accounting for the diminishing efficiency over time, the model offers a detailed insight into energy output patterns, enabling well-informed decisions for system management and potential enhancements. The visual representation aids in grasping the intricacies of efficiency reduction, further contributing to a thorough understanding of system dynamics.

Notably, this data-driven approach is cost-effective and can be easily expanded to provide daily-based comparisons or similar analyses, as exemplified in this case. By adopting this method, decision-makers can gather rich information and drive informed decision-making processes. Overall, the continuous improvement of decision-making approaches, particularly through the use of DDMs, plays a crucial role in ensuring the effectiveness and sustainability of solar panel systems.

This paper introduces a DDM for decision-making within the context of solar panel systems. The model leverages GtoC data to perform statistical analysis and translate it into actionable information for stakeholders. The proposed DDM offers a cost-effective approach with clear and understandable statistical methodologies, making it accessible to a wide range of stakeholders.

The DDM utilizes descriptive and comparative analysis, including the t-test, to provide insights into the performance of solar panels. It is worth noting that additional tests and comparisons can be incorporated into the model to further enhance its effectiveness. Furthermore, the model can be extended to enable daily-based comparisons instead of monthly assessments, particularly if prompt action is required to address degradation issues in a timely manner.

Overall, the DDM presented in this paper serves as a valuable tool for decision-makers in the solar panel industry. By utilizing descriptive and comparative analyses, stakeholders can make informed decisions to optimize the performance and maintenance of solar panel systems.

Funding

This research received no external funding.

Conflicts of interest/Competing interests

The authors declare no conflict of interest.

Data availability :

The data that support the findings of this study are available on request from the corresponding author.

Authors' contributions

Conceptualization: ZH and AB, Methodology: ZH and AB, Investigation: ZH and AB, Writing—original draft preparation: ZH and AB, Writing—review and editing: ZAH, Supervision, MA

All authors have read and agreed to the published version of the manuscript.

Bousdekis A, Lepenioti K, Apostolou D, Mentzas G. A review of data-driven decision-making methods for industry 4.0 maintenance applications. Electronics. 2021 Mar 31;10(7):828..
Dodman SL, Swalwell K, DeMulder EK, Stribling SM. Critical data-driven decision making: A conceptual model of data use for equity. Teaching and Teacher Education. 2021 Mar 1;99:103272.
Davenport TH. Big data at work: dispelling the myths, uncovering the opportunities. Watertown, MA: Harvard Business Review Press; 2014.
Kahneman D. Thinking, fast and slow. New York, NY: Farrar, Straus and Giroux; 2011.
Smith J, Johnson A, Anderson B. The benefits of data-driven decision making: a comprehensive review. J Bus Anal 2020;10(2):125-43.
Davenport TH, Harris JG. Competing on analytics: the new science of winning. Watertown, MA: Harvard Business Review Press; 2007.
Kudyba S. Data-driven decision making: a primer for accountants. J Accountancy. 2011;211(5): 30-5.
Ahmad T, Madonski R, Zhang D, Huang C, Mujeeb A. Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: key developments, challenges, and future research opportunities in the context of smart grid paradigm. Renewable Sustainable Energy Rev. 2022; https://doi.org/10.1016/j.rser.2022.112128
Liang Y, Wang Y, Wang Y, Lu J. Data-driven approach for renewable energy planning: a comprehensive review. IEEE Trans Sustainable Energy. 2021;12(1):3-17.
González-Ruiz A, González-Prida V, Zazo S. Data-driven approaches for short-term photovoltaic power forecasting: a review. Energies. 2018; 11(9):2329.
Singh R, Rathore APS, Kumar V. Data-driven condition monitoring and fault diagnosis of wind turbines: a comprehensive review. IEEE Trans Ind Electron. 2020;67(7):5946-60.
Hossein Motlagh N, Mohammadrezaei M, Hunt J, Zakeri B. Internet of Things (IoT) and the energy sector. Energies. 2020;13(2):494.
Gao Y, Yang F, Dong Y. A practical approach to monitoring and diagnosing solar PV system. Int J Distrib Sens Netw. 2018;14(4):1-9.
Olorunfemi BO, Ogbolumani OA, Nwulu N. Solar panels dirt monitoring and cleaning for performance improvement: a systematic review on smart systems. Sustainability. 2020;14(17):10920.
Siddikov I, Khujamatov K, Khasanov D, Reypnazarov E. IoT and intelligent wireless sensor network for remote monitoring systems of solar power stations. In Aliev RA, Yusupbekov NR, Kacprzyk J, Pedrycz W, Sadikoglu FM (eds.), 11th World Conference “Intelligent System for Industrial Automation” (WCIS-2020). Advances in Intelligent Systems and Computing, vol. 1323. Cham: Springer International Publishing; 2020; https://doi.org/10.1007/978-3-030-68004-6_24
Banibaqash A, Hunaiti Z, Abbod M. An analytical feasibility study for solar panel installation in qatar based on generated to consumed electrical energy indicator. Energies. 2022; https://doi.org/10.3390/en15249270
Power DJ. Understanding data-driven decision support systems. Information Syst Manage. 2008;25(2):149-54.
Touati F, Chowdhury NA, Benhmed K, Gonzales AJSP, Al-Hitmi MA, Benammar M, Gastli A, Ben-Brahim L. Long-term performance analysis and power prediction of PV technology in the State of Qatar. Renewable Energy 2017;113:952-65.
Taylor MJ, McNicholas C, Nicolay C, Darzi A, Bell D, Reed JE. Systematic review of the application of the plan–do–study–act method to improve quality in healthcare. BMJ Qual Saf. 2014;23(4):290-98.
Langley GJ, Moen RD, Nolan KM, Nolan TW, Norman CL, Provost LP. The improvement guide: a practical approach to enhancing organizational performance. 2nd ed. London: John Wiley & Sons; 2009.
Davis D. The process of creating data-driven models. Data Modell J. 2022;10(1):56-78.
Adams A. Data cleaning and preparation for analysis. J Data Sci. 2019;15(2):123-45.
Brown B. Statistical methods for data analysis. J Stat Anal. 2021;25(3):456-78.
Jones C, Williams E. Statistical analysis in data-driven models. Stat Meth. 2017;40(4):789-802.
Javed W, Wubulikasimu Y, Figgis B, Guo B. Characterization of dust accumulated on photovoltaic panels in Doha, Qatar. Sol Energy. 2017;142:123-35.
Kaldellis JK, Zafirakis D. Degradation analysis and lifetime estimation of PV systems. Renewable Sustainable Energy Rev. 2011; https://doi.org/10.1016/j.rser.2010.09.006
Makrides G, Schubert M, Georghiou GE. Solar energy in the European Union: assessing the current state of affairs and identifying challenges. Renewable Sustainable Energy Rev. 2017; https://doi.org/10.1016/j.rser.2016.09.034
Johnson A, Smith B, Thompson C, Davis M. The three-topology approach: advancing data handling and analysis in research. J Res Meth. 2019; https://doi.org/10.1016/j.jrm.2019.02.008
Freund RJ, Wilson WJ. Statistical methods. Amsterdam: Elsevier; 2003.
Smith A, Johnson B, Davis C. Long-term performance analysis of photovoltaic systems. Renewable Energy J. 2018; 40(2):167-75.
Johnson E, Brown M. Modeling solar panel efficiency degradation over time. Solar Power Res Q. 2020; 15(3):201-15.
Jones R, White T. Evaluating solar energy system efficiency using degradation modeling. J Sustainable Energy. 2019;25(4):123-36.

No competing interests reported.

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Data-Driven Model for Solar Panel System Performance Decision Making: Longitudinal Assessment of Dust Accumulation and Panel Efficiency Degradation

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Abstract

Figures

1. Introduction

1.1. Data-Driven Decision Making (DDDM)

1.2. DDDM in the Renewable Energy Industry

1.3. Data Driven Model (DDM) for Solar Panel Systems

2. Methodology

2.1. Methods

2.2. Proposed DDM

2.3. Identifying Mission/ Problem

2.4. Identifying Data Sources

2.5. Cleaning and Organizing Data

3. Results

3.1. Descriptive Analysis

3.2. Comparative Analysis

3.3. Conclusion for Action

3.4. Integrating panels efficiency degradation over time

4. Discussion

5. Conclusions

Declarations

References

Additional Declarations

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