Harnessing machine learning for short-to-long range weather forecasting: a Monterrey case study

Abstract

Weather forecasting is crucial in adapting and integrating renewable energy sources, particularly in regions with complex climatic conditions like Monterrey. This study aims to provide reliable weather prediction methodologies by evaluating the performance of various traditional Machine Learning models, including Random Forest Regressor (RFR), Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), and Recurrent Neural Networks (RNN) such as SimpleRNN, Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Cascade LSTM, Bidirectional RNNs, and a novel Convolutional LSTM/LSTM architecture that handles spatial and temporal data.

The research employs a dataset of historical weather data from Automatic Weather Stations and Advanced Baseline Imager Level 2 GOES-16 products, including key weather features like air temperature, solar radiation, wind speed, relative humidity, and precipitation. The models were trained and evaluated across different predictive ranges by combining distinct sampling and model output sizes.

This study’s findings underscore the effectiveness of the Cascade LSTM models, achieving a Mean Absolute Error of 1.6 °C for 72-hour air temperature predictions and 85.79 W/m2 for solar radiation forecasts. The ConvLSTM/LSTM model also significantly improves short-term predictions, particularly for solar radiation and humidity.

The main contribution of this work is a comprehensive methodology that can be generalized to other regions and datasets, supporting the nationwide implementation of localized machine-learning forecasting models. This methodology includes steps for data collection, preprocessing, creation of lagged features, and model implementation, as well as applying distinct approaches to forecasting by using autoregressive and fixed window models. This framework enables the development of accurate, region-specific forecasting models, facilitating better weather prediction and planning nationwide.