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Top 10 Solar Forecasting And Power Prediction Best Practices

It’s important to realize that accurate solar forecasting and solar prediction are crucial elements when optimizing solar energy to achieve its full utilization. In that case, by implementing best practices in solar forecasting and prediction, stakeholders can make informed decisions, improve energy planning, and enhance the integration of solar power into the power energy grid.

For decades, solar energy has taken an increasingly important part, which will continue to rise, driven by carbon peaking and carbon neutrality strategic goals, especially in energy consumption by many nations. On that note, due to the intermittence and volatility of sunlight, photovoltaic (PV) power generation is more erratic than other conventional power energy sources.

Eventually, this results in some problems in the grid: frequency instability, dispatch difficulty, and voltage and current surges. Hence, accurate power generation forecasting, particularly of the PV system, is one of the significant issues of the PV system’s engineering practice to settle the aforementioned problems. Efficient solar forecasting improves the grid connection ability.

In addition, solar prediction technology can still foster security, and it also effectively reduces light discarding. This guide presents the best practices for solar forecasting, highlighting key strategies to enhance accuracy and reliability.

Understanding What Solar Forecasting And Power Prediction Entails

At their core, Solar Forecasting and Power Prediction are weather and environmental problem-solution models. It entails modeling the Sun’s position concerning a given location at the Earth’s surface before then modeling the transmittance of solar radiation through the Earth’s atmosphere. Afterward, an accurate estimate of the solar radiation availability is then given.

In most cases, a power modeling software tool is used to generate predictions of the energy generation from a given solar facility. The most challenging aspect of modeling the transmittance of solar radiation through the atmosphere is correctly capturing the scattering and absorption aspects and noting the sunlight reflection that passes through the cloud cover.

As the share of solar energy in the energy mix increases, accurate solar power forecasting becomes more critical. The amount of solar energy being traded in energy markets is increasing rapidly. In many regions with no cost implications for inaccurate forecasts, imbalance penalties are now being introduced. Solar forecasts are through various primary categories.

More importantly, they are loosely organized by the forecasting time horizons they enable. They are the most commonly used metrics to evaluate solar power forecast errors. Still, experts are tweaking ways to improve the accuracy of these forecasts.

The forecast includes:
  1. Statistical Techniques
  2. Satellite Imagery Approaches
  3. Numerical Weather Prediction (Weather Models)

Generally speaking, according to a particular Solar Energy Journal, it’s worth mentioning that there are many benefits of solar power forecasting improvements. For example, it reduces the amount of electricity generation, especially from fast-reacting and lower-efficiency power plants, such as gas, oil GT, and IC. Additionally, it provides an annual economic value.

It also decreases ramping of all generators (start and shutdown costs) and solar power curtailment. The marginal value of solar power forecasting improvement increased with solar power penetration, while it decreased with additional improvement levels. And in reality… The Impact of DA Forecast Errors is crystal clear on Intraday Spot Pricing in Germany.

The Topmost Solar Forecasting And Power Prediction Best Practices

Like there are horses for courses, different forecasting techniques are more suitable depending on the intended forecast lead time. Ground-based and satellite-based methods offer the best forecast skill for a forecast lead time of a few hours ahead. Beyond that, NWP models provide the best forecast skills and combination models with better forecasts than individuals.

The most commonly used statistical error metrics are as follows:
  • Mean Absolute Error (MAE): MAE provides a good overview of average error. This is a valuable metric to understand the cost of inaccurate forecasts when the penalty for forecast error is linear. This metric is popular amongst energy traders.
  • Root Mean Square Error (RMSE): RMSE gives a higher weightage to significant forecast errors and is thus a suitable metric when the penalty for more significant forecast errors is heightened.
  • Mean Bias Error (MBE): MBE indicates whether a forecasting model, in general, tends to overestimate or underestimate in comparison to actual values. It should be noted that systematic deviations are easier to correct.

In addition to these metrics, forecasting experts often use several other error metrics to compare the performance of different models. However, these metrics may not always be relevant for understanding the cost implications of inaccurate forecasts. Below are the best solar forecasting practices, highlighting key strategies to enhance accuracy and reliability.

1. High-Quality Data Collection

Collecting high-quality solar radiation, meteorological, and historical data is essential for accurate solar prediction. Utilise reliable sensors and monitoring systems to capture high spatial and temporal resolution data.
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In addition, it’s also necessary to ensure proper instrument calibration and maintenance of the measurement equipment to avoid inaccuracies.

2. Comprehensive Data Analysis

Thoroughly analyze historical solar radiation and meteorological data to identify patterns and trends. Explore factors such as time of day, seasonality, and weather conditions to develop robust forecasting models. Employ statistical techniques, machine learning algorithms, and data visualization tools to extract valuable insights.

3. Numerical Weather Prediction 

Incorporate Numerical Weather Prediction (NWP) models into solar forecasting systems. NWP models simulate atmospheric conditions and provide valuable input for solar irradiance predictions. Combining NWP data with ground measurements can enhance forecast accuracy and extend prediction horizons.

4. Cloud Cover Monitoring

Accurate monitoring is crucial for solar prediction. Use ground-based sky cameras, satellite imagery, or both to monitor cloud movements and identify patterns. Incorporate this information into forecasting models to improve solar irradiance predictions during cloudy periods.

5. Ensemble Forecasting Techniques

Implement ensemble techniques that generate multiple forecasts using different models, initial conditions, and input parameters. Ensemble forecasts provide a range of possible outcomes, enabling better decision-making under uncertain conditions.

6. Real-Time Monitoring And Updates

Continuously monitor solar radiation, weather conditions, and real-time forecast performance. Update the forecasts regularly based on new observations and incorporate the latest data into prediction models. Real-time monitoring helps identify deviations from the estimates and allows for timely adjustments.
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7. Validation And Model Calibration

Regularly validate and calibrate forecasting models to ensure accuracy. Compare predicted values with actual measurements and identify any biases or errors. Adjust model parameters and algorithms based on validation results to improve forecast performance.

8. Effective Stakeholder Collaboration

Establish effective communication channels and collaboration frameworks with relevant stakeholders, including solar power plant operators, grid managers, and energy consumers. Exchange information, share data, and coordinate efforts to improve solar prediction accuracy and align energy planning.

9. Continuous Model Improvement

This includes updating and refining the critical solar forecasting models based on new data, technological advancements, and lessons from previous forecasts. Embrace emerging technologies such as artificial intelligence, machine learning, and big data analytics to enhance prediction capabilities.

10. Training And Expertise Development

Invest in training programs and knowledge-sharing initiatives to develop expertise in solar forecasting. Equip professionals with the necessary skills to analyze data, interpret forecast results, and make informed decisions based on solar predictions.

In Conclusion;

According to the modeling means of prediction, the prevailing PV power prediction methods are broadly divided into three categories. They are Artificial Intelligence (AI), physical techniques, and statistical forecasting technologies. Furthermore, various forecasting technologies are also provided, but PV forecasting technologies face other challenges.

First, it is difficult for physical forecasting technology to obtain accurate future weather forecast information and determine output characteristic model parameters. Second, statistical forecasting technology is not demanding for geographical location and other information of PV systems but requires masses of historical data to deduce statistics laws.

As for AI forecasting technology, it is easy to trap in the local optimum because of internal defects of the AI algorithm. The provided state-of-the-art solar energy forecasting journal clarifies the problems and gives some perspectives on various PV power prediction methods. It’s also part of ”Advanced Technologies for Planning and Operation of Prosumer Energy Systems.”

In a nutshell, it’s clear to note that the state-of-the-art solar energy forecasting journal is a detailed research case study from which you can borrow more ideas. One thing is for sure; it provides some additional guidelines with the best approaches (critical potentials and challenges) towards achieving the proper solar forecasting modeling means of solar power prediction.

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