Solar Power Generation Master Xiao

Can Xai be used for solar power generation forecasts?

The goal is to get a better understanding of how to apply XAI techniques to solar power generation forecasts and how to interpret "black box" machine learning models for usage in solar power station applications. In this paper, the Long-Short Memory (LSTM) is assumed to be the primary black-box model.

Can X-LSTM-EO predict solar power generation?

In conclusion, the proposed X-LSTM-EO model, along with the use of the XAI-based LIME algorithm, offers a more accurate and transparent method for predicting solar power generation in solar plant systems. These findings have important implications for developing and deploying renewable energy sources, such as solar power.

Can LSTM predict solar power generation under different environmental conditions?

In this paper the LSTM model is proposed to forecast the power generated by the solar system under different environmental conditions. The performance of LSTM is evaluated in comparison to that of Decision DT and LR.

What machine learning techniques are used in solar power forecasting?

The solar power forecasting task has previously used the k-nearest neighbor (KNN) machine learning technique . Boosting, bagging, and regression trees are other machine learning algorithms that have shown high accuracy and effectiveness.

Can a PSO optimizer accurately estimate PV power generation?

Additionally, the PSO optimizer was employed instead of the EO optimizer to validate the outcomes, which further demonstrated the efficacy of the EO optimizer. The experimental results and simulations demonstrate that the proposed model can accurately estimate PV power generation in response to abrupt changes in power generation patterns.

What are the trends in SW & son over China?

Generally, The trends in SW over China was projected to decrease in JJA, and SON over most of China during 2020–2099, however increasing trend was found in large areas in MAM and DJF (Figures 11d1 – 11d4 ).