Predicting solar energy yield is a multi-layered process that blends physics, meteorology, and engineering into one comprehensive model. At its core, you’re converting the sun’s available energy, modified by atmospheric conditions, into electrical power using the specifics of your photovoltaic (PV) system. Let’s break down the different components and approaches, based on different experts' opinions:

1. Understanding Solar Irradiance

• Solar Irradiance: This is the power per unit area received from the sun, typically measured in watts per square meter (W/m²). Begin by gathering historical and forecasted solar irradiance data from meteorological databases or satellite measurements.
• Clear-Sky Models: These models estimate the theoretical irradiance under ideal conditions (i.e., no clouds) and serve as a baseline. They are then adjusted using real-time or forecasted cloud cover data.

2. Acquiring and Analyzing Meteorological Data

• Weather Variables: To adjust the ideal clear-sky model, you need data on cloud cover, humidity, temperature, and atmospheric aerosols. These factors affect how much sunlight reaches your panels.
• Time-Based Variability: Solar energy is dynamic. Forecasting on an hourly, daily, or even seasonal basis requires time-series analysis where local weather forecasts are integrated with historical trends. This ensures that sudden changes—like an unexpected cloud front—are taken into account.

3. Modeling the PV System’s Performance

• System Parameters: Examine key attributes of your solar installation: Panel Area and Efficiency: Multiply the effective irradiance by the panel area and its conversion efficiency. Orientation and Tilt: The solar panels’ tilt and azimuth affect how much sunlight they receive. Use solar geometry based on your geographic location (latitude and longitude) to calculate the incident angle of sunlight throughout the day and year. Performance Ratio (PR): This factor accounts for losses due to wiring, inverter inefficiencies, soiling, temperature effects (since most panels degrade in performance when hot), and other system inefficiencies.
• The Basic Formula: A simplified equation is often used:

Energy Yield (kWh)=A×r×H×PR\text{Energy Yield (kWh)} = A \times r \times H \times PR

Where:

• AA = Total panel area (m²)
• rr = Module efficiency (a fraction)
• HH = Solar irradiation incident on the panel (kWh/m²)
• PRPR = Performance ratio (usually between 0.75 and 0.90)

4. Using Simulation Tools and Software

• Dedicated Software: Tools like the System Advisor Model (SAM) by NREL, PVsyst, and Helioscope simulate these complex interactions. They allow you to input local meteorological data, detailed system specifications, and even shading analysis.
• Shading Analysis: Even partial obstructions can significantly impact yield. Simulation software often includes features to model shading from nearby structures or vegetation over the course of the day or year.

5. Incorporating Advanced Forecasting Methods

• Physical Models vs. Empirical Models: Physical Models: Rely on well-understood physics and detailed meteorological inputs to predict irradiance. Empirical Models & Machine Learning: By using historical yield data and correlating it with weather parameters, you can create models using regression analysis or machine learning (like Random Forests or Neural Networks) to forecast yield. These models are valuable for short-term predictions where real-time data feeds improve accuracy.
• Hybrid Approaches: Some advanced methods combine physical models with machine learning to continuously calibrate predictions based on real-time feedback and historical performance.

6. Dealing with Uncertainty

• Error Margins: Even with advanced models, predicting solar yield is uncertain due to weather variability and system degradation over time.
• Sensitivity Analysis: Running simulations under different scenarios (e.g., various cloud cover conditions or system fault scenarios) can help understand the range of operational yields.
• Continuous Monitoring and Calibration: Leveraging actual production data from monitoring systems helps recalibrate your models continuously, ensuring predictions remain robust even as conditions or system performance evolve.

Based on the energy yield formula, through a measurement of solar irradiance in a certain area, multiply by area, efficiency and by the number of days. I can say that predicting solar energy yield involves estimating the total electricity that can be generated by a pv panel system over a certain period.

Based on the intensity of solar radiation and seasons also we can predict. and by evaluating the data

Predicting solar energy yield can be approached not only with complex models, but also with simple, robust methods that quantify uncertainty explicitly. In our recent work, we developed an interval-based forecasting method that uses only direct radiation and a historical similarity search to provide hourly prediction intervals.

How it works (short-term forecasts):

Advantages:

• Works with minimal inputs — no need for full meteorological datasets.
• Naturally provides upper and lower bounds for yield, helping quantify uncertainty.
• Robust under variable weather conditions.

We applied this to a real 4.8 kWp residential PV system in Spain, achieving high accuracy even in mixed weather scenarios. Details and figures of the method are here: Article Interval-Based Solar Photovoltaic Energy Predictions: A Sing...

Sometimes, simplicity is power — especially when you want transparent, explainable results without heavy computational requirements.