“Dust in the wind...”, deep learning application to wind energy time series forecasting

To balance electricity production and demand, it is required to use different prediction techniques extensively. Renewable energy, due to its intermittency, increases the complexity and uncertainty of forecasting, and the resulting accuracy impacts all the different players acting around the electricity systems around the world like generators, distributors, retailers, or consumers. Wind forecasting can be done under two major approaches, using meteorological numerical prediction models or based on pure time series input. Deep learning is appearing as a new method that can be used for wind energy prediction. This work develops several deep learning architectures and shows their performance when applied to wind time series. The models have been tested with the most extensive wind dataset available, the National Renewable Laboratory Wind Toolkit, a dataset with 126,692 wind points in North America. The architectures designed are based on different approaches, Multi-Layer Perceptron Networks (MLP), Convolutional Networks (CNN), and Recurrent Networks (RNN). These deep learning architectures have been tested to obtain predictions in a 12-h ahead horizon, and the accuracy is measured with the coefficient of determination, the R² method. The application of the models to wind sites evenly distributed in the North America geography allows us to infer several conclusions on the relationships between methods, terrain, and forecasting complexity. The results show differences between the models and confirm the superior capabilities on the use of deep learning techniques for wind speed forecasting from wind time series data.Peer ReviewedPostprint (published version

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

Predicting wind energy generation with recurrent neural networks

Decarbonizing the energy supply requires extensive use of renewable generation. Their intermittent nature requires to obtain accurate forecasts of future generation, at short, mid and long term. Wind Energy generation prediction is based on the ability to forecast wind intensity. This problem has been approached using two families of methods one based on weather forecasting input (Numerical Weather Model Prediction) and the other based on past observations (time series forecasting). This work deals with the application of Deep Learning to wind time series. Wind Time series are non-linear and non-stationary, making their forecasting very challenging. Deep neural networks have shown their success recently for problems involving sequences with non-linear behavior. In this work, we perform experiments comparing the capability of different neural network architectures for multi-step forecasting in a 12 h ahead prediction. For the Time Series input we used the US National Renewable Energy Laboratory’s WIND Dataset [3], (the largest available wind and energy dataset with over 120,000 physical wind sites), this dataset is evenly spread across all the North America geography which has allowed us to obtain conclusions on the relationship between physical site complexity and forecast accuracy. In the preliminary results of this work it can be seen a relationship between the error (measured as R2R2 ) and the complexity of the terrain, and a better accuracy score by some Recurrent Neural Network Architectures.Peer ReviewedPostprint (author's final draft

Smart Procurement of Naturally Generated Energy (SPONGE) for Plug-in Hybrid Electric Buses

We discuss a recently introduced ECO-driving concept known as SPONGE in the context of Plug-in Hybrid Electric Buses (PHEB)'s.Examples are given to illustrate the benefits of this approach to ECO-driving. Finally, distributed algorithms to realise SPONGE are discussed, paying attention to the privacy implications of the underlying optimisation problems.Comment: This paper is recently submitted to the IEEE Transactions on Automation Science and Engineerin

Risk-Averse Model Predictive Operation Control of Islanded Microgrids

In this paper we present a risk-averse model predictive control (MPC) scheme for the operation of islanded microgrids with very high share of renewable energy sources. The proposed scheme mitigates the effect of errors in the determination of the probability distribution of renewable infeed and load. This allows to use less complex and less accurate forecasting methods and to formulate low-dimensional scenario-based optimisation problems which are suitable for control applications. Additionally, the designer may trade performance for safety by interpolating between the conventional stochastic and worst-case MPC formulations. The presented risk-averse MPC problem is formulated as a mixed-integer quadratically-constrained quadratic problem and its favourable characteristics are demonstrated in a case study. This includes a sensitivity analysis that illustrates the robustness to load and renewable power prediction errors