Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features

Solar Generation Prediction using Artificial Intelligence: A Review

Solar energy generation is one of the most promising and fastest-growing renewable energy sources for the generation of useful energy worldwide. Forecasting of solar power is the most essential for the planning of grid operations, mainly in residential microgrids, to optimize and manage the energy produced in a dispatchable trend. Due to the inability of deterministic methods to accurately forecast solar power generation due to their dependency on natural inputs, Artificial Intelligence (AI) based techniques are required to be implemented.  AI techniques clubbed with stochastic methods are considered to be highly effective for solar generation forecasting. In this review, various artificial intelligence-based supervised and unsupervised learning methods for solar energy generation prediction are analyzed. The use of weather and environmental inputs for supervised learning is also compared. The accuracy of prediction of solar generation using several AI, Machine Learning, and Neural Network-based techniques are also analyzed in the paper. The paper presents an overall picture of the use of Artificial-Intelligence based techniques in solar generation prediction in the world