Collaborating Authors

New Hybrid Neuro-Evolutionary Algorithms for Renewable Energy and Facilities Management Problems Machine Learning

This Ph.D. thesis deals with the optimization of several renewable energy resources development as well as the improvement of facilities management in oceanic engineering and airports, using computational hybrid methods belonging to AI to this end. Energy is essential to our society in order to ensure a good quality of life. This means that predictions over the characteristics on which renewable energies depend are necessary, in order to know the amount of energy that will be obtained at any time. The second topic tackled in this thesis is related to the basic parameters that influence in different marine activities and airports, whose knowledge is necessary to develop a proper facilities management in these environments. Within this work, a study of the state-of-the-art Machine Learning have been performed to solve the problems associated with the topics above-mentioned, and several contributions have been proposed: One of the pillars of this work is focused on the estimation of the most important parameters in the exploitation of renewable resources. The second contribution of this thesis is related to feature selection problems. The proposed methodologies are applied to multiple problems: the prediction of $H_s$, relevant for marine energy applications and marine activities, the estimation of WPREs, undesirable variations in the electric power produced by a wind farm, the prediction of global solar radiation in areas from Spain and Australia, really important in terms of solar energy, and the prediction of low-visibility events at airports. All of these practical issues are developed with the consequent previous data analysis, normally, in terms of meteorological variables.

Feature Engineering and Forecasting via Integration of Derivative-free Optimization and Ensemble of Sequence-to-sequence Networks: Renewable Energy Case Studies Machine Learning

This research introduces a framework for forecasting, reconstruction and feature engineering of multivariate processes. We integrate derivative-free optimization with ensemble of sequence-to-sequence networks. We design a new resampling technique called additive which along with Bootstrap aggregating (bagging) resampling are applied to initialize the ensemble structure. We explore the proposed framework performance on three renewable energy sources wind, solar and ocean wave. We conduct several short- to long-term forecasts showing the superiority of the proposed method compare to numerous machine learning techniques. The findings indicate that the introduced method performs reasonably better when the forecasting horizon becomes longer. In addition, we modify the framework for automated feature selection. The model represents a clear interpretation of the selected features. We investigate the effects of different environmental and marine factors on the wind speed and ocean output power respectively and report the selected features. Moreover, we explore the online forecasting setting and illustrate that the model exceeds alternatives through different measurement errors.

Tackling Climate Change with Machine Learning Artificial Intelligence

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

Green power runs up against desert conservation in California

Los Angeles Times

California lawmakers' grand ambitions to fight climate change are running into a familiar obstacle: the parochial concerns of local governments and property owners. The latest battle over state needs vs. local control is being fought in San Bernardino County, where the Board of Supervisors voted last month to ban solar and wind farms across vast stretches of rural desert communities. The decision was cheered by residents who have complained that the proliferation of large renewable energy projects threatened to wipe out their scenic vistas and upend the fragile desert ecosystem. San Bernardino County's ban comes just as California is supposed to be dramatically ramping up its renewable energy usage as part of the state's effort to slash the carbon emissions that promote climate change. Last year, Gov. Jerry Brown signed a bill requiring utility companies to get 60% of their electricity from renewable sources by 2030, and 100% from zero-carbon sources by 2045.

To Combat Climate Change, We Gotta Get a Better Battery. But How?


Casual observers of clean energy are often surprised by its limitations. Greenhouse gas emissions have fallen in California by 13 percent since 2004, even as the economy has grown by more than a quarter. That unprecedented reduction is partly the result of increased dependence on natural gas, which is cleaner than coal, but mostly because of the falling costs of renewable energy. The change is wonderful: Between 2008 and 2015, the price that energy utilities paid for solar energy fell by 77 percent, and wind decreased 47 percent, according to a report by the California Public Utilities Commission. Those falling costs encouraged energy providers to construct solar and wind farms all over the state.