A Machine Learning Forecast of Renewable Solar Power Generation and Analysis of Distribution and Management Using IOT-Based Sensor Data

Consumption and generation of renewable energy play pivotal roles in global energy dynamics, with an increasing emphasis on eco-friendly and sustainable practices. This research paper aims to examine the trends in energy consumption and renewable energy generation in selected countries beginning with an analysis of the historical trends in energy consumption for major economies such as China, the United States, India, and others; the research investigates the future of energy consumption. The paper investigates the growth rates of energy consumption over time and identifies specific years that deviated significantly from the overall trend. In addition, it examines the impact of various economic alliances, such as BRICS and OECD, on the landscape of energy consumption. In order to improve the precision of predictions and forecasts, this study employs a rigorous methodology that involves manual hyperparameter optimization. In conjunction with this, we utilize the predictive potential of several advanced regression techniques based on machine learning. To model and forecast the time series energy consumption data, specifically, Lasso regression and tree-based gradient boosting regressors are used. Through meticulous calibration of hyperparameters, we optimize the performance of our predictive models, thereby ensuring superior accuracy and robustness in predicting future energy values. Lasso regression facilitates feature selection and regularization, thereby minimizing overfitting and augmenting model generalizability. In the meantime, tree-based gradient boosting regressors utilize ensemble algorithms to capture complex nonlinear relationships within time series data. The findings of this study provide valuable insights into the historical energy consumption patterns of the world’s leading economies and the ascendance of renewable energy sources.