Long-term forecasting and analysis of PM2.5, a significant air pollution source, is vital for environmental governance and sustainable development. We evaluated 10 machine learning and deep learning models using PM2.5 concentration data along with environmental variables. Employing explainable AI (XAI) technology facilitated explainability and formed the basis for factor analysis. At a 30-day forecasting horizon, ensemble learning surpassed deep learning in performance, with CatBoost emerging as the top-performing model. For forecasting horizons of 90 and 180 days, Bi-SLTM and Bi-GRU, respectively, exhibited the highest performance. Through an analysis of influencing factors by SHAP, it was observed that PM10 exerted the greatest impact on PM2.5 forecasting. However, this effect was particularly pronounced at higher concentrations of CO. Conversely, at lower CO concentrations, the impact of increased PM10 concentrations on PM2.5 was limited. Hence, it can be inferred that CO plays a pivotal role in driving these effects. Following CO, factors such as “dew point” and “temperature” were identified as influential. These factors exhibited varying levels of linear correlation with PM2.5, with temperature showing a negative correlation, while PM10, CO, and dew point generally demonstrated positive correlations with PM2.5.

The application of black-box models, namely ensemble and deep learning, has significantly advanced the effectiveness of solar power generation forecasting. However, these models lack explainability, which hinders comprehensive investigations into environmental influences. To address this limitation, we employ explainable artificial intelligence (XAI) techniques to enhance the interpretability of these black-box models, while ensuring their predictive accuracy. We carefully selected 10 prominent black-box models and deployed them using real solar power datasets. Within the field of artificial intelligence, it is crucial to adhere to standardized usage procedures to guarantee unbiased performance evaluations. Consequently, our investigation identifies LightGBM as the model that requires explanation. In a practical engineering context, we utilize XAI methods to extract understandable insights from the selected model, shedding light on the varying degrees of impact exerted by diverse environmental factors on solar power generation. This approach facilitates a nuanced analysis of the influence of the environment. Our findings underscore the significance of “Distance from the Noon” as the primary factor influencing solar power generation, which exhibits a clear interaction with “Sky Cover.” By leveraging the outcomes of our analyses, we propose optimal locations for solar power stations, thereby offering a tangible pathway for the practical.

In this work, energy time series forecasting competitions from the Schneider Company, the Kaggle Online platform, and the American society ASHRAE were considered. These competitions include power generation and building energy consumption forecasts. The datasets used in these competitions are based on reliable and real sensor records. In addition, exogenous variables are accurately added to the dataset. All of these ensure the richness of the information contained in the dataset, which is crucial for energy management. Therefore, (1) We choose to study forecast models suitable for energy management on these energy datasets; (2) Forecast models including popular algorithm structures such as neural network models and ensemble models. In addition, as an innovation, we introduce the Explainable AI method (SHAP) to explain models with excellent performance indicators, thereby strengthening its trust and transparency; (3) The results show that the performance of the integrated model in these competitions is more stable and efficient, and in the integrated model, the advantages of LightGBM are more obvious; (4) Through the interpretation of SHAP, we found that the lagging characteristics of the building area and target variables are important features.

In this paper, we present a novel approach to defining the solution of a differential game. This approach allows to define optimal strategies in the differential games when players do not have the full information about the structure of the game along the time interval [t₀, T] on which the game is defined. It assumed that at each time instant the players have information about the structure of the game (payoff functions, motion equations) only for the next fixed time interval T̅̅, which is called the information horizon. Based on this information the players make their decision. Also in this work we consider the case when the players have the opportunity to control the informational horizon in order to receive the better joint payoff for the cooperating players. The problem is to define T̅̅ such that the imputation of each player computed on the basis of restricted information deviates from the imputation in classical cooperative differential game (with full information about the structure of the game known at the beginning of the game) by a value that does not exceed given M. The developed concept is applied to a cooperative differential game of pollution control.