The situation at the scene of the fire is changing rapidly. How to collect and analyze the most immediate fire information, providing the most effective information for disaster decision-making has always been an important issue. This paper proposes an Intelligent Fire Point Sensing System (IFPSS), which proposes fire condition prediction based on artificial intelligence technology as well as large amounts of gas and temperature data in fire scenes collected by IoT devices. The IFPSS collected actual gas and temperature data from the simulation room where the actual fire test was conducted. Taking carbon monoxide (CO) and hydrogen sulfide (H<sub>2</sub>S) data as an example, the artificial intelligence analysis of IFPSS uses linear regression algorithm to establish artificial intelligence model. After training and testing the model, an accuracy of up to 84.4% predicts whether the fire process is in the very early stages of a fire.
With the large-scale deployment of solar photovoltaic (PV) installation, managing the efficiency of the generation system has become essential. One of the main challenges facing solar PV power output lies in the difficulty in managing solar irradiance fluctuation. Generally speaking, the power output is heavily influenced by solar irradiance and sky conditions which are consistently changing. Thus, the ability to accurately forecast the solar PV power is critical for optimizing the generation system and ensuring the quality of service. In this paper, we propose a solar PV forecasting model using Recurrent Neural Network (RNN) in a Cascade model combined with Hierarchical Clustering for improving the overall prediction accuracy of solar PV forecast. The proposed model, upon comparing with other learning algorithms, namely, Feed-forward Artificial Neural Network (FFNN), GRU, Support Vector Regression (SVR) and K Nearest Neighbors (KNN) using the cluster data from K-Means Clustering and Hierarchical Clustering, had the lowest average NRMSE of 8.88% using Hierarchical clustered data. According to the results, Hierarchical Clustering suits better for solar PV forecast than K-means clustering.