Applications of machine learning for temperature prediction

Many organizations currently struggle greatly with temperature forecasting. The agricultural, ecological, environmental, and industrial sectors all take temperature forecasting very seriously. Planning actions that support the protection of lives and property is crucial for the public, business, and government. The development of solar energy systems, adaptive temperature control in greenhouses, predictions of building cooling and energy use, and the evaluation and forecasting of environmental dangers all benefit from precise temperature forecasting. A planning horizon for company expansion, energy policy, and infrastructure upgrades might be established with the aid of accurate temperature predictions when paired with the analysis of other parameters in the area of interest. A current topic of scientific interest is adapting machine-learning algorithms for temperature prediction. In this research, applications of machine learning algorithms for temperature prediction are studied and used. Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest (RF), etc. are among the machine learning algorithms used in numerous research for prediction tasks; nonetheless, MLR and RF are the widely used ones. To conduct a thorough comparative analysis of prediction techniques, multiple linear regression and random forest are utilized. The data set consist of 807 observations with 2 target features: average surface temperature and outlet temperature and 6 input features namely radiation from the sun, air temperature, humidity, wind speed, inlet temperature and system on or off. This study demonstrates that using a collection of input features, such as past measurements of air temperature, wind speed, and inlet temperature, among others, machine learning algorithms can assist in properly forecasting temperatures. Both algorithms do an excellent job of predicting the target feature, however our research shows that RF makes less errors than MLR. Therefore, based on our observations, RF predicts temperature more accurately than MLR.