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.
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.
If you are concerned about climate change, then you should take note of this: Over the last eight months, utilities from New York to Nebraska have announced plans to shutter six nuclear reactors by 2019. These closures will come on the heels of earlier ones -- five reactors have been shuttered in the last three years alone. The latest closure announcement came earlier this month when Exelon Corp., the country's largest nuclear-energy producer, said it would close three reactors at two sites in Illinois by 2018. The six targeted reactors have been safely producing about 40 terawatt-hours of zero-carbon-emissions electricity per year (one terawatt-hour equals 1 billion kilowatt-hours). These reactors' output exceeds the amount of zero-carbon electricity produced annually by every solar energy installation in the nation.
Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. When Congress passed a budget bill in February, you may have missed something significant: It includes new incentives to support carbon capture and storage, or CCS. If you've heard of CCS before, you've probably heard that it's expensive, risky, and unnecessary in light of the recent progress of wind and solar energy. Compared with renewable energy, CCS--a technology that captures carbon dioxide emissions from fossil fuel sources and injects them deep into the earth for permanent storage--has always been the ugly duckling of climate change mitigation. Even though the Intergovernmental Panel on Climate Change has found that we are unlikely to meet our climate targets without it, CCS has received little policy support and, consequently, seen minimal deployment.
No contour of California's vast landscape inspires such passionate devotion as its coastline, so state lawmakers recoiled when President Trump announced in April that he wanted to expand offshore drilling. The outrage was channeled into a proposal for preventing any new infrastructure along the water, pipelines or otherwise, for additional oil production. But the day before a key Sacramento committee hearing this summer, Sen. Hannah-Beth Jackson (D-Santa Barbara) received some bad news about her legislation -- it was opposed by a politically powerful labor group whose members' paychecks depend on the steady flow of oil. In a letter to lawmakers, the top lobbyist for the State Building and Construction Trades Council of California said he feared harming projects that "maintain and create new employment opportunities." The legislation, Senate Bill 188, stalled the following day, an unceremonious defeat for a proposal announced with much fanfare months earlier.