Innovative Approaches in Residential Solar Electricity: Forecasting and Fault Detection Using Machine Learning
Abstract
:1. Introduction
2. Literature Survey
3. Residential Solar Electricity System
- Sunlight intensity: The amount of sunlight that reaches the solar panels directly affects their power output. Regions with high solar irradiance, such as deserts, typically produce more electricity than areas with frequent cloud cover.
- Temperature: While solar panels require sunlight to generate electricity, high temperatures can reduce their efficiency. Most solar panels operate optimally at around 25 ·C (77 ·F), and their performance decreases with rising temperatures due to increased resistance in the photovoltaic cells.
- Panel orientation and tilt: The angle and direction in which solar panels are installed significantly impact their efficiency. Panels should ideally be oriented towards the south (in the Northern Hemisphere) or the north (in the Southern Hemisphere) to maximize sunlight exposure. The tilt angle should match the latitude of the location to optimize performance throughout the year.
- Shading: Shading from nearby trees, buildings, or other obstructions can drastically reduce the efficiency of solar panels. Even partial shading can impact the entire panel’s output because most panels are interconnected in series, where shading one cell affects the entire string.
- Air quality: Air pollution and particulate matter can settle on the surface of solar panels, obstructing sunlight and reducing efficiency. Regular cleaning and maintenance are essential to ensure optimal performance.
- System losses: Several types of losses can occur in a solar PV system, including shadow losses, temperature losses, DC and AC cable losses, inverter losses, and dust and dirt accumulation.
4. Methodology to Forecast Solar Panel Power Output
- Weather Data: Historical and real-time data on sunlight intensity, cloud cover, temperature, and humidity. These parameters significantly impact the amount of solar radiation received by the panels.
- Air Quality Data: Information on particulate matter, dust, and other pollutants that can reduce the efficiency of solar panels by obstructing sunlight.
- Solar Panel Data: Historical performance data of the solar panels, including power output, voltage, and current measurements.
- Cleaning: Removing or correcting any erroneous or missing data points. Techniques such as interpolation can be used to estimate missing values.
- Normalization: Scaling the data to a standard range to ensure that all features contribute equally to the model. Common normalization techniques include min-max scaling and z-score normalization.
- Aggregation: Aggregating data into appropriate time intervals (e.g., hourly or daily) to match the granularity of the forecasting model.
- Sunlight intensity;
- Cloud cover;
- Temperature;
- Humidity;
- Particulate matter levels;
- Historical power output.
- Linear Regression: A simple and interpretable model that assumes a linear relationship between the input features and the target variable.
- Decision Trees: Non-linear models that partition the feature space into regions with similar output values.
- Random Forests: An ensemble of decision trees that improves prediction accuracy and reduces overfitting.
- Gradient Boosting Machines (GBMs): An ensemble technique that builds models sequentially to correct the errors of the previous models.
- Neural Networks: Deep learning models capable of capturing complex non-linear relationships in the data.
- Mean Absolute Error (MAE): The average absolute difference between the predicted and actual values.
- Mean Squared Error (MSE): The average of the squared differences between the predicted and actual values.
- Root Mean Squared Error (RMSE): The square root of the Mean Squared Error, providing an indication of the typical prediction error magnitude.
- R-squared (R2): A statistical measure that indicates the proportion of variance in the dependent variable explained by the independent variables.
- Threshold-based Methods: Defining acceptable ranges for performance metrics and flagging deviations beyond these thresholds.
- Anomaly Detection Algorithms: Using machine learning models to identify unusual patterns in the data that differ from normal operating conditions.
- Diagnostic Models: Leveraging historical fault data to train models that can diagnose specific issues based on observed symptoms.
5. Leveraging Machine Learning Techniques
- Linear Regression: Linear Regression is a foundational algorithm in statistical modeling, used to understand the relationship between dependent and independent variables by fitting a linear equation to observed data. It utilizes the least-squares method to minimize the sum of the squares of the differences between the observed and predicted values. Linear Regression is particularly effective when there is a linear relationship between the variables, making it a straightforward yet powerful tool for predictive analytics.
- AdaBoost: Adaptive Boosting (AdaBoost) is an ensemble learning technique that improves the accuracy of predictive models by combining multiple weak learners into a strong ensemble. AdaBoost iteratively adjusts the weights of misclassified instances, focusing more on difficult cases in subsequent iterations. This method is known for its ability to enhance the performance of various base algorithms, particularly when dealing with complex datasets and improving the overall robustness of the prediction model.
- Decision Tree: Decision Trees are intuitive models that split the data into subsets based on the value of input features, forming a tree-like structure. Each node represents a decision rule, while each branch represents the outcome of the rule, and leaf nodes represent the final prediction. Decision Trees are highly interpretable and can handle both categorical and numerical data, making them suitable for various types of prediction problems, including classification and regression tasks.
- k-Nearest Neighbor (kNN): kNN is a non-parametric algorithm that predicts the value of a new instance based on the majority class or average of its k-Nearest Neighbors in the feature space. It is simple to implement and particularly useful for pattern recognition and classification tasks. The effectiveness of kNN relies on the choice of k and the distance metric, which can be optimized to improve prediction accuracy.
- Support Vector Machine (SVM): SVM is a powerful supervised learning algorithm that constructs hyperplanes in a multidimensional space to separate different classes. The objective is to find the optimal hyperplane that maximizes the margin between different classes, thereby minimizing classification errors. SVM is particularly effective in high-dimensional spaces and when the number of dimensions exceeds the number of samples, making it suitable for complex datasets.
- Random Forest: Random Forest is an ensemble learning method that operates by constructing multiple decision trees during training and outputting the mode of the classes (classification) or mean prediction (regression) of the individual trees. It provides insights into variable importance and handles datasets with numerous features effectively, reducing overfitting and improving accuracy.
- Multilayer Perceptron (MLP): MLP, a type of artificial Neural Network, consists of multiple layers of nodes with nonlinear activation functions, which allow the network to model complex relationships in the data. MLPs are flexible and can capture intricate patterns, making them suitable for a wide range of prediction tasks.
6. Results
- R-squared (R2)R-squared (R2) is a statistical measure representing the proportion of variance in the dependent variable (actual power output) explained by the independent variables (predicted power outputs). It ranges from 0 to 1, where a value closer to 1 indicates a better fit of the model to the data. Mathematically, it is defined as
- is the actual value;
- is the predicted value;
- is the mean of the actual values;
- n is the number of observations.
- Mean Squared Error (MSE)Mean Squared Error (MSE) measures the average squared difference between the predicted values and the actual values. Lower MSE values indicate better model performance. Mathematically, it is defined as
- is the actual value;
- is the predicted value;
- n is the number of observations.
R2 is more reliable with larger datasets, while MSE is more reliable for smaller datasets or when focusing on the accuracy of individual predictions.
- Solar Power Generation Over Time (Figure 6a): This chart reveals that solar power generation fluctuates significantly throughout the day, with noticeable peaks and troughs. The pattern shows higher power generation during daylight hours and lower power generation during early morning and late evening, which is consistent with the expected solar activity.
- Average Solar Power per Hour (Figure 6b): The average solar power per hour chart indicates that the peak production typically occurs around mid-day when the sun is at its highest. This insight is crucial for energy planning and resource allocation, suggesting that maximum solar power can be harnessed during these hours.
- Variance of Solar Power per Hour (Figure 6c): The variance chart highlights the stability of solar power generation at different times. Higher variance during certain hours indicates more significant fluctuations, which could be due to varying weather conditions or other environmental factors. Lower variance during mid-day hours suggests more reliable and consistent solar power generation.
- Solar Power Generation with Peak Times Highlighted (Figure 6d): This chart identifies the exact times when solar power generation peaks, marked by red dots. These peak periods are crucial for maximizing energy capture and can inform strategies for energy storage and grid management to balance supply and demand effectively.
- Temperature Over Time, Figure 7a: The top-left plot shows the variation in temperature over time. The x-axis represents time, marked in hours from 00:00 to 06:00, and the y-axis represents temperature in degrees Celsius. The line plot shows fluctuations in temperature over the given period, ranging between approximately 15 ·C and 35 ·C.
- Humidity Over Time, Figure 7b: The top-right plot illustrates the changes in humidity over time. Similar to the first plot, the x-axis represents time in hours, and the y-axis represents humidity as a percentage. The humidity data show significant variation, with values ranging from 45% to 80%.
- Cloud Cover Over Time, Figure 7c: The bottom-left plot presents the cloud cover data over time. The x-axis represents time in hours, and the y-axis represents cloud cover, with values ranging from 0 to 1. The plot indicates varying cloud cover throughout the period, with frequent fluctuations.
- Wind Speed Over Time, Figure 7d: The bottom-right plot shows the wind speed over time. The x-axis represents time in hours, and the y-axis represents wind speed in kilometers per hour. The wind speed data exhibit fluctuations, with values ranging from approximately 1 km/h to 9 km/h.
7. Analysis and Comparison of Fault Detection Models
- k-Nearest Neighbor emerged as the top performer, with the highest accuracy and F1-score, and the quickest response time of 1.2 s, making it ideal for real-time fault detection in solar systems.
- Random Forest also showed excellent performance, slightly trailing behind k-Nearest Neighbor in accuracy and response time, making it another strong candidate for fault detection.
- AdaBoost and Support Vector Machine (SVM) performed well, with high accuracy and reasonable response times, although they did not outperform the top models.
- Neural Network (ANN), while robust, exhibited slightly lower precision and recall, indicating potential room for improvement in feature selection or model tuning.
- Linear Regression, while the simplest model, showed the lowest performance across most metrics, making it less suitable for complex fault detection tasks in this context.
8. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Kuzemko, C.; Bradshaw, M.; Bridge, G.; Goldthau, A.; Jewell, J.; Overland, I.; Scholten, D.; Van de Graaf, T.; Westphal, K. COVID-19 and the politics of sustainable energy transitions. Energy Res. Soc. Sci. 2020, 68, 101685. [Google Scholar] [CrossRef]
- Beniwal, R.; Kalra, S.; SinghBeniwal, N.; Gupta, H.O. Smart photovoltaic system for Indian smart cities: A cost analysis. Environ. Sci. Pollut. Res. 2023, 30, 45445–45454. [Google Scholar] [CrossRef] [PubMed]
- Beniwal, R.; Kalra, S.; Beniwal, N.S.; Mazumdar, H.; Singhal, A.K.; Singh, S.K. Walk-to-Charge Technology: Exploring Efficient Energy Harvesting Solutions for Smart Electronics. J. Sens. 2023, 2023, 6614658. [Google Scholar] [CrossRef]
- Bello, U.; Livingstone, U.; Abdullahi, A.M.; Sulaiman, I.; Yahuza, K.M. Renewable energy transition: A panacea to the ravaging effects of climate change in Nigeria. Aceh Int. J. Sci. Technol. 2021, 10, 182–195. [Google Scholar] [CrossRef]
- Zohuri, B. Navigating the global energy landscape balancing growth, demand, and sustainability. J. Mat. Sci. Appl. Eng. 2023, 2, 1–7. [Google Scholar]
- Rathore, N.; Panwar, N.L.; Yettou, F.; Gama, A. A comprehensive review of different types of solar photovoltaic cells and their applications. Int. J. Ambient. Energy 2021, 42, 1200–1217. [Google Scholar] [CrossRef]
- Kumari, N.; Singh, S.K.; Kumar, S. A comparative study of different materials used for solar photovoltaics technology. Mater. Today Proc. 2022, 66, 3522–3528. [Google Scholar] [CrossRef]
- Efaz, E.T.; Rhaman, M.M.; Imam, S.A.; Bashar, K.L.; Kabir, F.; Mourtaza, M.E.; Sakib, S.N. A review of primary technologies of thin-film solar cells. Eng. Res. Express 2021, 3, 032001. [Google Scholar] [CrossRef]
- Yu, S. Designing solid-state electrolytes for safe, energy-dense batteries. Nat. Mater. 2021, 20, 1142–1150. [Google Scholar]
- Xie, Y. Lithium-sulfur batteries: Advances and challenges. Adv. Energy Mater. 2020, 10, 1902878. [Google Scholar]
- Smith, A. Battery storage technologies for grid-scale renewable energy integration. Energy Storage Mater. 2022, 25, 1–10. [Google Scholar]
- Kim, J. A review of silicon anode materials for lithium-ion batteries. J. Power Sources 2020, 472, 228568. [Google Scholar]
- Wang, Q. Solid-state batteries: Materials and challenges. Energy Storage Sci. Technol. 2020, 9, 745–758. [Google Scholar]
- Zhao, L. High-performance lithium-ion batteries: Recent advancements and perspectives. Energy Sci. Eng. 2022, 10, 656–675. [Google Scholar]
- Song, Z.; Liu, J.; Yang, H. Air pollution and soiling implications for solar photovoltaic power generation: A comprehensive review. Appl. Energy 2021, 298, 117247. [Google Scholar] [CrossRef]
- Travieso-González, C.M.; Cabrera-Quintero, F.; Piñán-Roescher, A.; Celada-Bernal, S. A Review and Evaluation of the State of Art in Image-Based Solar Energy Forecasting: The Methodology and Technology Used. Appl. Sci. 2024, 14, 5605. [Google Scholar] [CrossRef]
- Aliferis, C.; Simon, G. Overfitting, Underfitting and General Model Overconfidence and Under-Performance Pitfalls and Best Practices in Machine Learning and AI. In Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfalls; Springer: Cham, Switerland, 2024. [Google Scholar]
- Kurani, A.; Doshi, P.; Vakharia, A.; Shah, M. A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting. Ann. Data Sci. 2023, 10, 183–208. [Google Scholar] [CrossRef]
- Zideh, M.J.; Chatterjee, P.; Srivastava, A.K. Physics-informed machine learning for data anomaly detection, classification, localization, and mitigation: A review, challenges, and path forward. IEEE Access 2023, 12, 4597–4617. [Google Scholar] [CrossRef]
- Machín, A.; Márquez, F. Advancements in Photovoltaic Cell Materials: Silicon, Organic, and Perovskite Solar Cells. Materials 2024, 17, 1165. [Google Scholar] [CrossRef]
- Dharmadasa, I.M.; Alam, A.E. How to Achieve Efficiencies beyond 22.1% for CdTe-Based Thin-Film Solar Cells. Energies 2022, 15, 9510. [Google Scholar] [CrossRef]
- Powalla, M.; Paetel, S.; Ahlswede, E.; Wuerz, R.; Wessendorf, C.D.; Magorian Friedlmeier, T. Thin-film solar cells exceeding 22% solar cell efficiency: An overview. AIP Adv. 2020, 5, 041602. [Google Scholar]
- Renogy. Bifacial Solar Panels: Everything You Need to Know. 2024. Available online: https://www.renogy.com/blog/bifacial-solar-panels-disadvantages-and-advantages/#:~:text=Bifacial%20solar%20panels%20can%20be,boosts%20their%20overall%20energy%20output (accessed on 23 July 2024).
- Marsh, J. Bifacial Solar Panels: What You Need to Know. 2024. Available online: https://www.energysage.com/solar/bifacial-solar-panels-what-you-need-to-know/ (accessed on 23 July 2024).
- Kopecek, R.; Libal, J. Bifacial Photovoltaics 2021: Status, Opportunities and Challenges. Energies 2021, 14, 2076. [Google Scholar] [CrossRef]
- Akinyele, D.O.; Rayudu, R.K.; Padayachee, N.N. Energy Storage Technologies for Residential Applications: Impacts and Prospects. Renew. Sustain. Energy Rev. 2017, 68, 1105–1117. [Google Scholar]
- Luthander, P.; Widén, J.; Nilsson, D.; Palm, J. Photovoltaic self-consumption in buildings: A review. Appl. Energy 2015, 142, 80–94. [Google Scholar] [CrossRef]
- Nair, M.G.; Garimella, J.; Venkatesh, S.P. Recent Advances in Solid-State and Flow Battery Technologies for Energy Storage Applications. J. Energy Storage 2020, 27, 100827. [Google Scholar]
- Salas, A.; Carbone, R.; Hernandez, M.V. Smart Inverters for Improved Grid Stability in Residential PV Systems. IEEE Trans. Smart Grid 2019, 10, 5678–5686. [Google Scholar]
- Ahmed, K.; Abdel-Salam, M.A.; El-Fayoumi, S. Implementation of Smart Inverters in Residential Solar Systems. Renew. Energy 2021, 169, 127–136. [Google Scholar]
- Lopes, J.P.; Hatziargyriou, N.; Mutale, J.; Djapic, P.; Jenkins, N. Integrating distributed generation into electric power systems: A review of drivers, challenges and opportunities. Electr. Power Syst. Res. 2006, 77, 1189–1203. [Google Scholar] [CrossRef]
- Kassem, A.; Said, S.A.M.; Al-Sulaiman, M.F. Grid-Connected PV Systems: Applications and Challenges in Hot Climates. Renew. Sustain. Energy Rev. 2021, 58, 219–235. [Google Scholar]
- Martin, C.; Koffi, J.M.; Brandt, A. Advanced Grid Integration of Residential Solar Power: Challenges and Opportunities. Energy Rep. 2020, 6, 45–60. [Google Scholar]
- Taylor, M. Residential Solar PV Systems: A Comparative Review of Technologies and Cost Structures. Renew. Energy 2021, 155, 1235–1245. [Google Scholar]
- Harper, J. Advances in Solar PV Technology: Trends and Perspectives. J. Renew. Sustain. Energy 2020, 12, 041301. [Google Scholar]
- Chen, S. Energy Efficiency of Residential Solar PV Systems. Energy Procedia 2019, 159, 246–251. [Google Scholar]
- Li, X. Recent Developments in Perovskite Solar Cells. Adv. Energy Mater. 2019, 9, 1803246. [Google Scholar]
- Zhang, Y. Life Cycle Assessment of Residential Solar Photovoltaic Systems. Renew. Energy 2020, 150, 302–312. [Google Scholar]
- Boxwell, M. Solar Electricity Handbook: A Simple Practical Guide to Solar Energy: How to Design and Install Solar Photovoltaic Systems; Greenstream Publishing: Coventry, UK, 2017. [Google Scholar]
- Arbab-Zavar, B.; Palacios-Garcia, E.J.; Vasquez, J.C.; Guerrero, J.M. Smart inverters for microgrid applications: A review. Energies 2019, 12, 840. [Google Scholar] [CrossRef]
- Ueda, Y.; Kurokawa, K.; Kitamura, K.; Yokota, M.; Akanuma, K.; Sugihara, H. Performance analysis of various system configurations on grid-connected residential PV systems. Sol. Energy Mater. Sol. Cells 2009, 93, 945–949. [Google Scholar] [CrossRef]
- Essam, Y.; Ahmed, A.N.; Ramli, R.; Chau, K.-W.; Idris Ibrahim, M.S.; Sherif, M.; Sefelnasr, A.; El-Shafie, A. Investigating photovoltaic solar power output forecasting using machine learning algorithms. Eng. Appl. Comput. Fluid Mech. 2022, 16, 2002–2034. [Google Scholar] [CrossRef]
- Zazoum, B. Solar photovoltaic power prediction using different machine learning methods. Energy Rep. 2022, 8, 19–25. [Google Scholar] [CrossRef]
- Platon, R.; Martel, J.; Woodruff, N.; Chau, T.Y. Online fault detection in PV systems. IEEE Trans. Sustain. Energy 2015, 6, 1200–1207. [Google Scholar] [CrossRef]
- Mohana, M.; Saidi, A.S.; Alelyani, S.; Alshayeb, M.J.; Basha, S.; Anqi, A.E. Small-scale solar photovoltaic power prediction for residential load in Saudi Arabia using machine learning. Energies 2021, 14, 6759. [Google Scholar] [CrossRef]
Model Name | Special Characteristics | Inputs | Total Samples | Training Samples | Validation Samples | Prediction Samples | Performance Metrics | PV Panels | Inverter | Sensors and Batteries |
---|---|---|---|---|---|---|---|---|---|---|
Linear Regression | Simple, interpretable, assumes linearity | 6 | 10,000 | 7000 | 2000 | 1000 | R2: 0.92, MSE: 0.04 | Monocrystalline, 400 W | Smart Inverter, 5 kW | Pyranometer, Temp., Humidity, PM2.5, Lithium-ion, 10 kWh |
Random Forest | Handles non-linearity, reduces overfitting | 6 | 10,000 | 7000 | 2000 | 1000 | R2: 0.95, MSE: 0.02 | Monocrystalline, 400 W | Smart Inverter, 5 kW | Pyranometer, Temp., Humidity, PM2.5, Lithium-ion, 10 kWh |
Neural Network | Captures complex patterns, high computational cost | 6 | 10,000 | 7000 | 2000 | 1000 | R2: 0.94, MSE: 0.03 | Monocrystalline, 400 W | Smart Inverter, 5 kW | Pyranometer, Temp., Humidity, PM2.5, Lithium-ion, 10 kWh |
k-Nearest Neighbors | Simple, effective for pattern recognition | 6 | 10,000 | 7000 | 2000 | 1000 | R2: 0.93, MSE: 0.035 | Monocrystalline, 400 W | Smart Inverter, 5 kW | Pyranometer, Temp., Humidity, PM2.5, Lithium-ion, 10 kWh |
Support Vector Machine | High-dimensional data handling, complex tuning | 6 | 10,000 | 7000 | 2000 | 1000 | R2: 0.91, MSE: 0.05 | Monocrystalline, 400 W | Smart Inverter, 5 kW | Pyranometer, Temp., Humidity, PM2.5, Lithium-ion, 10 kWh |
AdaBoost | Ensemble method, improves weak learner performance | 6 | 10,000 | 7000 | 2000 | 1000 | R2: 0.93, MSE: 0.034 | Monocrystalline, 400 W | Smart Inverter, 5 kW | Pyranometer, Temp., Humidity, PM2.5, Lithium-ion, 10 kWh |
Decision Tree | Non-linear model, intuitive, interpretable | 6 | 10,000 | 7000 | 2000 | 1000 | R2: 0.90, MSE: 0.06 | Monocrystalline, 400 W | Smart Inverter, 5 kW | Pyranometer, Temp., Humidity, PM2.5, Lithium-ion, 10 kWh |
Ref. No. | Study | Findings |
---|---|---|
[20] | Machín and Márquez (2024) | Advancements in photovoltaic cell materials have led to efficiencies exceeding 22%. |
[21] | Dharmadasa and Alam (2022) | Techniques to achieve efficiencies beyond 22.1% for CdTe-based thin-film solar cells. |
[22] | AIP Advances (2020) | Overview of thin-film solar cells exceeding 22% efficiency. |
[23] | Renogy (2024) | Bifacial solar panels capture light from both sides, increasing energy yield by up to 30%. |
[24] | EnergySage (2024) | Bifacial solar panels offer increased efficiency and better low-light performance. |
[25] | Kopecek and Libal (2021) | Bifacial PV systems can achieve up to 40% more energy yield compared to monofacial systems. |
[26] | Akinyele et al. (2017) | Lithium-ion batteries are popular for residential energy storage due to their high energy density and long cycle life. |
[27] | Luthander et al. (2015) | Effective energy storage systems are crucial for balancing supply and demand in residential solar installations. |
[28] | Nair et al. (2020) | Emerging storage technologies, such as solid-state and flow batteries, offer potential improvements in energy density, safety, and cost. |
[29] | Salas et al. (2019) | Smart inverters enhance grid stability and provide advanced features like reactive power control and voltage regulation. |
[30] | Ahmed et al. (2021) | Implementation of smart inverters improves energy management and allows for better grid integration. |
[31] | Lopes et al. (2016) | Advanced inverter functionalities support the integration of higher levels of distributed solar generation without compromising grid stability. |
[32] | Kassem et al. (2021) | Grid-tied solar systems allow homeowners to feed excess electricity back into the grid, promoting energy independence and reducing electricity costs. |
[33] | Martin et al. (2018) | Effective integration of residential solar power into existing grids requires advanced grid management techniques and infrastructure upgrades. |
[34] | Uddin et al. (2019) | Smart grid technologies and energy management systems are critical for integrating distributed solar power. |
[35] | Sharma et al. (2020) | Machine learning algorithms can predict solar power output, enabling more efficient planning and utilization of solar resources. |
[36] | Li et al. (2019) | Neural Networks and advanced machine learning techniques aid in fault detection and predictive maintenance in solar power generation. |
[37] | Mohammed et al. (2021) | Real-time monitoring and management of residential solar systems using machine learning improves operational efficiency and reduces downtime. |
Test Dataset Size (%) | ANN | KNN |
---|---|---|
30% | 0.75 | 0.9999926 |
40% | 0.72 | 0.9999919 |
50% | 0.74 | 0.9999849 |
Metric | Linear Regression | Support Vector Machine | k-Nearest Neighbor | Random Forest | AdaBoost | Neural Network (ANN) |
---|---|---|---|---|---|---|
Accuracy | 93.50% | 97.80% | 99.85% | 99.65% | 98.90% | 98.45% |
Precision | 92.30% | 97.50% | 99.90% | 99.70% | 98.60% | 97.80% |
Recall | 91.80% | 97.60% | 99.80% | 99.60% | 98.70% | 98.10% |
F1-Score | 92.05 | 97.55 | 99.85 | 99.65 | 98.65 | 97.95 |
Response Time | 2.0 s | 1.8 s | 1.2 s | 1.3 s | 1.5 s | 1.5 s |
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Share and Cite
Kalra, S.; Beniwal, R.; Singh, V.; Beniwal, N.S. Innovative Approaches in Residential Solar Electricity: Forecasting and Fault Detection Using Machine Learning. Electricity 2024, 5, 585-605. https://doi.org/10.3390/electricity5030029
Kalra S, Beniwal R, Singh V, Beniwal NS. Innovative Approaches in Residential Solar Electricity: Forecasting and Fault Detection Using Machine Learning. Electricity. 2024; 5(3):585-605. https://doi.org/10.3390/electricity5030029
Chicago/Turabian StyleKalra, Shruti, Ruby Beniwal, Vinay Singh, and Narendra Singh Beniwal. 2024. "Innovative Approaches in Residential Solar Electricity: Forecasting and Fault Detection Using Machine Learning" Electricity 5, no. 3: 585-605. https://doi.org/10.3390/electricity5030029
APA StyleKalra, S., Beniwal, R., Singh, V., & Beniwal, N. S. (2024). Innovative Approaches in Residential Solar Electricity: Forecasting and Fault Detection Using Machine Learning. Electricity, 5(3), 585-605. https://doi.org/10.3390/electricity5030029