Solar Software When Combined with Artificial Technology – A New Trend in 2021?
- Renewable energy is in high demand nowadays. More and more structures require solar software to improve their business projects, agricultural activities, and other corporate processes. 2019-2020 is the period of the PV-panel boom in the world when more and more enterprises began to use renewable energy resources more often and in a bigger quantity.
Solar PV Energy Panels & Machine Learning
According to the latest research, the number of PV solar energy panel installations increased by 20% worldwide in 2020. These statistics are going to grow as renewable energy experts state. The solar software for energy input prediction based on artificial technologies can provide forecasting data more accurately and quickly. Moreover, machine-learning algorithms provide short-term forecasts that allow representatives and analytics of the renewable energy sector to draw conclusions and form strategies faster and risk-free as well.
Speaking about solar power output produced by the PV panels, it is worth mentioning that improved predictions can solve the most actual issues of the agricultural sector, enterprises, and other figures of the energy sector. Machine-learning models deliver solutions for the following cases:
- Predictions according to various weather regimes at once;
- Forecasting data based on a genetic algorithm (when the natural selection processes are taken into account);
- Improved accuracy level on the historical energy output levels;
- Combined predictions both on solar and wind energy input according to hybrid AI algorithms.
Ready-Done AI Models for Solar Software to Implement
The Swedish forecasting machine-algorithm model TST Reikard is based on the short-time solar power input predictions. Due to the AI-technologies and neural networks formed for forecasting purposes, the solar software provides accurate predictions for a short period of time. Additionally, the UV-irradiance is captured with the help of the machine-learning model over a 24-hour period. It means that the solar PV energy panels together with AI-based solutions can bring more accurate predictions according to numerous weather, irradiance, and power input factors. The key information on the TST Reikard solution:
- For more accurate predictions the maximal short-term horizons are used. These terms vary from 0 to 4 hours.
- Momentary weather changes are taken into account. It means that specific weather regimes are used during the machine-learning modeling process.
- The comprehensive forecasting effect is achieved with the help of historical energy output levels of the PV panels. When all the data is analyzed and modeled, more accurate predictions are produced by the solar forecasting software.
It is worth noting that the AI-based modeling solar software developed by the Swedish company is improved with one more algorithm. All the weather regimes are divided into several groups, so the special-purpose subdivisions take part in the modeling process. This factor is called genetic. While combined with other modeling datasets, it offers renewable energy analysts to form one absolutely applicable model for the current weather regimen, seasonal changes, and other aspects that take place in the short-term time segment.
Solar Tracking Software & Deep Learning Techniques
Speaking about solar tracking software, Sun and its apparent trajectory are very necessary for the accurate showings and following analytical research. For this purpose, deep learning techniques are used. Machine learning offers the use of several types of tracking facilities (pocket screens, host computers, etc.) and the implementation of various neural networks at once.
The Case of the AI Framework Use for Solar Tracking Software
Tensorflow offered a new-era approach to the procedure of solar energy tracking with the help of machine learning algorithms. The AI facilities simplify the solar software implementation and improve the tracking showing accuracy levels. The Sun’s apparent trajectory is taken into account and well-tracked with the help of the deep learning techniques to be implemented.
Additionally, it is possible to improve the solar software performance with the help of the implementation of several neural networks at once. One more benefit is the flexibility of the AI-based facilities. Any device can become the tracking unit that makes this solution a multipurpose one for the renewable energy sector.
It goes without saying that solar software develops constantly in the present-day reality. One of the latest trends to take into consideration is the implementation of machine learning in the renewable energy sector. This way tracking, monitoring, and other solar software are improved and provide more accurate metrics and faster proceeding. Let’s take a closer look at the AI solutions that could turn out the ready-done algorithms into new-era facilities.
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