Location Agnostic Source-Free Domain Adaptive Learning to Predict Solar Power Generation

The prediction of solar power generation is a challenging task due to its dependence on climatic characteristics that exhibit spatial and temporal variability. The performance of a prediction model may vary across different places due to changes in data distribution, resulting in a model that works well in one region but not in others. Furthermore, as a consequence of global warming, there is a notable acceleration in the alteration of weather patterns on an annual basis. This phenomenon introduces the potential for diminished efficacy of existing models, even within the same geographical region, as time progresses. In this paper, a domain adaptive deep learning-based framework is proposed to estimate solar power generation using weather features that can solve the aforementioned challenges. A feed-forward deep convolutional network model is trained for a known location dataset in a supervised manner and utilized to predict the solar power of an unknown location later. This adaptive data-driven approach exhibits notable advantages in terms of computing speed, storage efficiency, and its ability to improve outcomes in scenarios where state-of-the-art non-adaptive methods fail. Our method has shown an improvement of $10.47 \%$, $7.44 \%$, $5.11\%$ in solar power prediction accuracy compared to best performing non-adaptive method for California (CA), Florida (FL) and New York (NY), respectively.

Location Agnostic Adaptive Rain Precipitation Prediction using Deep Learning

Rain precipitation prediction is a challenging task as it depends on weather and meteorological features which vary from location to location. As a result, a prediction model that performs well at one location does not perform well at other locations due to the distribution shifts. In addition, due to global warming, the weather patterns are changing very rapidly year by year which creates the possibility of ineffectiveness of those models even at the same location as time passes. In our work, we have proposed an adaptive deep learning-based framework in order to provide a solution to the aforementioned challenges. Our method can generalize the model for the prediction of precipitation for any location where the methods without adaptation fail. Our method has shown 43.51%, 5.09%, and 38.62% improvement after adaptation using a deep neural network for predicting the precipitation of Paris, Los Angeles, and Tokyo, respectively.

Comparative Evaluation of Weather Forecasting using Machine Learning Models

Gaining a deeper understanding of weather and being able to predict its future conduct have always been considered important endeavors for the growth of our society. This research paper explores the advancements in understanding and predicting nature's behavior, particularly in the context of weather forecasting, through the application of machine learning algorithms. By leveraging the power of machine learning, data mining, and data analysis techniques, significant progress has been made in this field. This study focuses on analyzing the contributions of various machine learning algorithms in predicting precipitation and temperature patterns using a 20-year dataset from a single weather station in Dhaka city. Algorithms such as Gradient Boosting, AdaBoosting, Artificial Neural Network, Stacking Random Forest, Stacking Neural Network, and Stacking KNN are evaluated and compared based on their performance metrics, including Confusion matrix measurements. The findings highlight remarkable achievements and provide valuable insights into their performances and features correlation.