Rolnick, David, Donti, Priya L., Kaack, Lynn H., Kochanski, Kelly, Lacoste, Alexandre, Sankaran, Kris, Ross, Andrew Slavin, Milojevic-Dupont, Nikola, Jaques, Natasha, Waldman-Brown, Anna, Luccioni, Alexandra, Maharaj, Tegan, Sherwin, Evan D., Mukkavilli, S. Karthik, Kording, Konrad P., Gomes, Carla, Ng, Andrew Y., Hassabis, Demis, Platt, John C., Creutzig, Felix, Chayes, Jennifer, Bengio, Yoshua
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.
The automotive industry is rushing to produce electric vehicles (EVs) as the world tries to move away from polluting hydrocarbons to greener, cleaner fuels. But EVs still only account for 1% of the total market. Some companies are betting that hydrogen fuel cells will be the power source of the future, but Nissan believes bio-ethanol produced from sugar cane or corn could also produce zero-emission electric energy. Nissan recently unveiled its prototype solid-oxide fuel cell vehicle in Brazil, where ethanol is readily available in all gas stations - in marked contrast to hydrogen pumps. But Nissan boss Carlos Ghosn says the success of the concept will largely depend on political support.
In clean energy news, General Electric just announced that America's first offshore wind farm will be completed by the end of this year. China kicked its environmental initiatives up a notch with plans to triple its solar power capacity by the year 2020. Tesla has discontinued its 10kWh Powerwall home battery as it prepares to launch a new model this summer. A tiny house village for the homeless in Oregon received a solar energy upgrade, and we spotted a handy photovoltaic "Lifepack" that keeps your gadgets powered on the go. Producing water from thin air sounds like a magic trick, but that's exactly what the Warka Water Tower does, and this week the project took home a World Design Impact Prize.
Most of the current game-theoretic demand-side management methods focus primarily on the scheduling of home appliances, and the related numerical experiments are analyzed under various scenarios to achieve the corresponding Nash-equilibrium (NE) and optimal results. However, not much work is conducted for academic or commercial buildings. The methods for optimizing academic-buildings are distinct from the optimal methods for home appliances. In my study, we address a novel methodology to control the operation of heating, ventilation, and air conditioning system (HVAC). With the development of Artificial Intelligence and computer technologies, reinforcement learning (RL) can be implemented in multiple realistic scenarios and help people to solve thousands of real-world problems. Reinforcement Learning, which is considered as the art of future AI, builds the bridge between agents and environments through Markov Decision Chain or Neural Network and has seldom been used in power system. The art of RL is that once the simulator for a specific environment is built, the algorithm can keep learning from the environment. Therefore, RL is capable of dealing with constantly changing simulator inputs such as power demand, the condition of power system and outdoor temperature, etc. Compared with the existing distribution power system planning mechanisms and the related game theoretical methodologies, our proposed algorithm can plan and optimize the hourly energy usage, and have the ability to corporate with even shorter time window if needed.