Machine Learning Brings Accuracy to Climate Forecasts

#artificialintelligence

The increasing number of satellites and advancements in climate models has improved the weather forecasting over the last many years. Weather forecasting, is not a perfect science; it still needs a lot of improvement in terms of timing, location, and intensity of forecast weather. The same goes for analyzing climate change. And, the prime reason behind this is lack of data to make more accurate forecasts. Global warming researchers face lack of important data.


Primitive climate models dating back to the 1970s were 'impressively accurate', rebutting sceptics

Daily Mail - Science & tech

Primitive climate models dating back to as early as the 1970s have turned out to be largely accurate, rebutting the long-running doubts of sceptics, a study has found. Experts assessed 17 old models -- including one that brought the issue of climate change to public light -- to see how accurate their temperature predictions were. Time must pass before model predictions can be compared with the actual average global temperatures, as short-term variations can obscure the real trend. The team found that most of the discrepancies between the studies and real-word figures came not from errors in the models but from unexpected emissions levels. The findings, the researchers conclude, provide reassurance that models being used today are likely to be reliable as well.


Advice Provision for Energy Saving in Automobile Climate-Control System

AI Magazine

Reducing energy consumption of climate control systems is important in order to reduce human environmental footprint. Our approach takes into account both the energy consumption of the climate control system and the expected comfort level of the driver. We therefore build two models, one for assessing the energy consumption of the climate control system as a function of the system's settings, and the other, models human comfort level as a function of the climate control system's settings. Using these models, the agent provides advice to the driver considering how to set the climate control system.


Azaria

AAAI Conferences

Reducing energy consumption of climate control systems is important in order to reduce human environmental footprint. The need to save energy becomes even greater when considering an electric car, since heavy use of the climate control system may exhaust the battery. In this paper we consider a method for an automated agent to provide advice to drivers which will motivate them to reduce the energy consumption of their climate control unit. Our approach takes into account both the energy consumption of the climate control system and the expected comfort level of the driver. We therefore build two models, one for assessing the energy consumption of the climate control system as a function of the system's settings, and the other, models human comfort level as a function of the climate control system's settings. Using these models, the agent provides advice to the driver considering how to set the climate control system. The agent advises settings which try to preserve a high level of comfort while consuming as little energy as possible. We empirically show that drivers equipped with our agent which provides them with advice significantly save energy as compared to drivers not equipped with our agent.


Climate Prediction via Matrix Completion

AAAI Conferences

Recently, machine learning has been applied to the problem of predicting future climates, informed by the multi-model ensemble of physics-based climate models that inform the Intergovernmental Panel on Climate Change (IPCC). Past work (Monteleoni et al., 2011, McQuade and Monteleoni, 2012) demonstrated the promise of online learning algorithms applied to this problem. Here we propose a novel approach, using sparse matrix completion.