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Abstract Citizen science data for monitoring air pollution have recently emerged as a powerful yet under-explored resource to complement expensive and sparse national air quality monitors. In urban environments, these new data have the potential to allow for high-resolution and high-frequency forecasts, and thereby to provide an assessment of population exposure at neighbourhood level. The complex spatio-temporal structure of these data, however, requires new flexible methods that are also able to provide timely forecasts. In this work, we propose a novel method that first provides forecasts with a reservoir computing approach, an echo-state network, adjusts the forecast with a transformer network with attention mechanism and then merges the echo-state and transformer forecast into a combined network. The stochastic nature of the method allows for a fast and more accurate forecast then individual predictors as well as standard statistical methods. Simulation and application to San Francisco air pollution show how the proposed method is able to produce high-resolution urban maps of air quality. Additionally, we show how these forecasts can be used to provide neighbour-level exposure assessment using population data, a task that would not be achievable with sparse government-sponsored air quality networks. 

An accurate and timely assessment of wind speed and energy output allows an efficient planning and management of this resource on the power grid. Wind energy, especially at high resolution, calls for the development of nonlinear statistical models able to capture complex dependencies in space and time. This work introduces a Convolutional Echo State AutoencodeR (CESAR), a spatio‐temporal, neural network‐based model which first extracts the spatial features with a deep convolutional autoencoder, and then models their dynamics with an echo state network. We also propose a two‐step approach to also allow for computationally affordable inference, while also performing uncertainty quantification. We focus on a high‐resolution simulation in Riyadh (Saudi Arabia), an area where wind farm planning is currently ongoing, and show how CESAR is able to provide improved forecasting of wind speed and power for proposed building sites by up to 17% against the best alternative methods. 

Abstract: In the past decades, clean and renewable energy has gained increasing attention due to a global effort on carbon footprint reduction. In particular, Saudi Arabia is gradually shifting its energy portfolio from an exclusive use of oil to a reliance on renewable energy, and, in particular, wind. Modelling wind for assessing potential energy output in a country as large, geographically diverse and understudied as Saudi Arabia is a challenge which implies highly non-linear dynamic structures in both space and time. To address this, we propose a spatio-temporal model whose spatial information is first reduced via an energy distance-based approach and then its dynamical behaviour is informed by a sparse and stochastic recurrent neural network (Echo State Network). Finally, the full spatial data is reconstructed by means of a non-stationary stochastic partial differential equation-based approach. Our model can capture the fine scale wind structure from a high-resolution simulation and produce more accurate forecasts of both wind speed and energy in lead times of interest for energy grid management.