REVIEW 2022 - AGENDA PAW CLIMATE

For decades, climate activists would tell you that profiting from helping the planet is an oxymoron – and this used to be largely true, but has quickly become an outdated stereotype in the past five years. As consumer preferences, regulatory pressure, and public markets place increasing premiums on lowering emissions, climate tech entrepreneurs are uniquely well positioned to generate tremendous shareholder value. In this session I will discuss these recent market forces and highlight the lessons from climate tech companies who have achieved massive commercial success and done well by doing good, such as Apeel, Aurora Solar, and Farmers Business Network, and from building my own company, Therma.

A fast-moving video montage of professionals and students who are operating at the intersection of machine learning and climate & sustainability.

Today, there’s still a crude understanding of the operational efficiency of materials recovery facilities, where recyclables are processed—weigh trucks on the way in and weigh commodities on the way out. Facilities cannot report the purity of the products with certainty without a tractable way to measure it aside from breaking open bales and auditing quality periodically. An accurate observer could deduce this by counting contamination prior to its deposit into a bunker, but this is financially intractable and operationally burdensome.

However, a computer vision system that can accurately detect everything it sees offers a solution, and it’s gaining traction. Operators are installing AI-powered computer vision systems throughout their facilities to analyze the inputs and outputs of relevant parts of the process to create a holistic view of where material is flowing, and most importantly, what’s in it. This development is making it possible to observe with individual scrutiny the inputs and outputs of a plant to inform actions that influence these quantities. As an industry, we’re still in the early stages of unlocking the power of AI, but better observability will lead to insights and closed-loop actions that continue to make current recycling operations more efficient.

Estimating CO2 emissions from power plants is no easy task. And as with any challenging project, data quality is key to its success. No matter how complex and state-of-the-art a model is, it won’t be very good unless there is data in sufficient quantity and with adequate processing. Thus, this talk aims to demonstrate how we at TransitionZero and Climate TRACE rely on thorough analysis, dataset iteration and other data-centric tools to detect CO2 emissions of power plants from satellite imagery.

At Regrow we are building the products and tech which enable farmers to receive payments for climate-positive food production. However, these carbon markets, including the products and tech which enable them, are just the beginning. Additional ecosystem service markets have the potential to transform agriculture by incentivising sustainable practices. These markets can be enabled by reliable and high resolution information on agricultural practices at continental scales, which can only be achieved by applying the latest machine learning techniques to large satellite imagery datasets. Join this session to learn how cutting-edge machine learning and satellite data can support carbon markets, and how this tech will enable new products and markets in the future.

Every 5 minutes consumers and generators of electricity in Australia’s wholesale National Electricity Market must decide how much power to buy or sell at a given price. When there is an oversupply of electricity the market price becomes negative, costing generators millions of dollars per year. Until recently renewable generators assumed these negative price periods as a cost of doing business, but the increased frequency of negative price periods and the deployment of utility scale batteries has made the application of highly adaptive time series forecasting essential to integrating renewable energy and battery storage into the NEM. We will describe how Fluence uses Machine Learning to efficiently operate these renewable energy assets.

NCX maintains a high-resolution map of the forests of the United States to underpin our forest carbon marketplace. Recent work has focused on transitioning this from an annual data product to a quarterly updated dataset, testing the integration and combination of various machine learning techniques. We’ll present successes and failures, and relative impacts of processing remote sensing data using Deep Markov models with Pyro, deep sequence models like RNN’s, CNN’s, and transformers, and varying forms of image representation using VAE’s. We will present our evaluation of time, cost, and impact on company goals of precise, robust carbon accounting.

Comprehensive electrification of transportation is a crucial element of decarbonizing the world economy.  But the ability to design, test, and manufacture new batteries is already becoming a bottleneckin the race to carbon-neutral transportation. Battery development and manufacturing is a complex process that requires considerations of power, energy density, cost, form-factor, and safety. We will discuss the ways in which Toyota Research Institute has used ML to drive improvements across the lifecycle of this process, including basic materials science research, battery design, and ML-driven manufacturing and quality control, in accordance with kaizen, the Toyota principle of “continuous improvement”.

New materials are critical to unlocking technological innovations to fight climate change. However, the status quo for bringing these materials to market is often a decades-long process.  To meet global sustainability needs and sharply curb global emissions, we must bring breakthrough products to market faster and more sustainably.  At Citrine Informatics, we are working toward this mission by deploying ML for materials discovery at some of the world’s leading materials and chemicals companies. In this talk, I’ll outline Citrine’s approach to ML-driven materials discovery and share some case studies of its application to sustainability-oriented use-cases.

Climate change is increasing the frequency and severity of extreme weather events. In order to quantify the impacts of climate on financial systems, improved climate models are needed with much higher spatial resolution than is currently possible. Moreover the models must be able to accurately quantify uncertainty in the frequency and severity of impacts under different climate forcing scenarios. Terrafuse AI is developing deep learning-based climate models trained on large Earth observation and simulation data sets, deployed on cloud computing infrastructure. With this approach our aim is to develop a “digital twin” of the climate system and its societal impacts so that appropriate adaptation and resilience strategies can be developed.

Batteries enable portable energy and as such they are ubiquitous in modern daily life and a key component for societal adaptation to climate change.  Voltaiq is committed to empowering this transition to electrification. We will cover three case studies overviewing machine learning uses in commercial battery development. We start with an illustrative case study on data form, volume, and collection method at each step throughout battery development. Next we will deep-dive and provide a case study on the use of machine learning to predict key performance metrics for a battery. We end with a study of machine learning to identify features in battery data.

Join us for this deeply technical conversation on geospatial machine learning in climate: the relevant ML methods, challenges, data, and applications to planning and monitoring of nature-based solutions, climate risk, and more.

AI and Robotics are both dramatically shifting our industrial capabilities and opening new doors to our functional understanding and ways of supporting the natural world. Together these advances can enable something far beyond simply limiting our damage to the planet – they create the possibility of building a new relationship with nature, wherein our industrial footprint can be radically reduced and nature’s capability to support itself and all life on Earth (including us!) can be amplified.

Join us for this deeply technical conversation on machine learning for green energy: the relevant ML methods, challenges, data, and applications to energy supply/demand forecasting, grid management, energy efficiency, and more.

Cement is responsible for 8% of worldwide CO2 emissions. Fortunately, the footprint can be reduced by 60% if burnt limestone, the main ingredient, is replaced partly by limestone powder. However, such low-carbon recipes react much more sensitive to changes in the chemical and mineralogical composition, limiting reliable production to laboratory conditions.

Alcemy is changing this. Robert presents a Machine Learning control and data analytics case study to optimize production processes of multiple plants such that low-carbon cement and concrete can be manufactured in real plants and at scale.

Myst AI has over three years of experience delivering highly accurate forecasts to organizations in clean power like climate-conscious load-serving entities, renewable power providers, and more. This talk will discuss some of the key ingredients we’ve identified as critical to delivering ongoing accuracy in our deployed load forecasting models. We’ll speak in particular to solutions to real-world data complexities: cross-validation in a domain where upstream forecasts are themselves critical features and historical data update over time, as well as ensembling approaches that solve unique challenges around data availability.

Transportation industry is responsible for 29% of the carbon emissions in the United States. In Convoy we use cutting edge technology to play a part in reducing waste in the trucking industry. Routing in particular, can be optimized to reduce the empty miles that trucks drive. In the network efficiency team we use Machine Learning to learn from driver’s routing behavior and Operations Research to optimally plan their work.

Better, cheaper and more abundant batteries will be a key driver of a faster transition to sustainability.  Improving batteries requires pushing the boundaries and empirically comparing many variations on design.  Therefore, quantifying and comparing important issues and performance metrics enables faster improvements. In this session, we use neural nets and various data-oriented processes to extract such a metric  from the raw timeseries cycling data from scratch,  and present our learnings integrating various ideas such as structured labels, ensembles, distillation, various active learning techniques as well as model-assisted quality control of data to tame this challenging data modality.

Soil-based carbon sequestration has attracted substantial attention due to its massive potential capacity and relative ease of implementation. However, scalable and accurate soil measurement methods have been elusive, making verification of sequestration a notable barrier. We present an ML-based approach relying on multispectral satellite imagery as a scalable, robust measurement methodology for soil carbon on farmland. The solution performs above existing standards under testing in the USA and Australia, and at a much lower cost. The audience will learn the background on carbon credit markets and soil-based sequestration, as well as select model implementation details and real-world test results.

At Open Climate Fix we aim to use ML to address climate change. We do this by connecting researchers using the most recent ML learning models with industry practitioners. The first challenge we are addressing is Solar nowcasting. Solar energy is predicted to be the largest form of generation globally by 2040. and having accurate real-time forecasts is hugely important to balance the energy system in a carbon effective way. We are delivering this service for the grid operator in the UK, National Grid. This talk shows the ML techniques we used to tackle this problem.

Recycling is one of the primary levers we can use to fight our society’s impact on our climate, and the industry has tremendous potential to improve its operations and drive real impact in our fight against climate change.

Glacier is deploying cutting-edge ML to perform classification and detection work that is revolutionizing the circular economy. Our challenges are unique and complex – distinguishing plastic resins, characterizing truckloads of trash, automatically detecting contamination. We’re using ML to extract value out of the products we consume and throw away, to ensure that businesses in the space have the technology they need to thrive, and to make an immediate impact on our climate.

Granular, multi-year time series of building-level electricity consumption are fundamental to distribution planning processes to support the adoption of solar panels, battery storage, and electric vehicles. These data are often measured using smart meters. However, some electric utilities do not have the hardware or data transmission processes in place to collect this key dataset. The absence of time series data can slow the adoption of distributed energy resources by making it difficult to prepare for their impacts on distribution grid infrastructure. This session covers Kevala’s load simulation tool, a modeling system used to synthesize granular electricity consumption time series data at the building level. The load simulation tool relies on static features, such as parcel attributes, and time-varying features, such as weather, to simulate hourly demand values over arbitrary time spans. Kevala’s load simulation tool learns relationships between these features and the scale and temporal variability of measured energy consumption values from smart meter readings. The results of Kevala’s load simulation tool have been used as a basis for modeling how the proliferation of solar panels, battery storage, and electric vehicles impacts electricity consumption.

At One Concern, we develop models for digital twins and their resilience by employing machine learning and advanced statistical methods to build a platform where organizations, communities, and private and public sectors understand, forecast, and mitigate climate risk. This session will cover how One Concern develops digital twins and resilience models by applying machine learning algorithms.

The quality and disclosure of corporate carbon emissions is critical in benchmarking progress towards net-zero emission targets for regulators and the wider market. in addressing climate change. We investigate the use of machine-learning algorithms to predict and forecast corporate carbon footprints, in particular we compare ML models for Scope 1, 2 and 3 emissions, comparing their performance against traditional OLS linear models. We forecast carbon footprints based on the availability of predictors in a three-step framework (Historical Model, Energy Model and Financial Model).  We find that machine learning algorithms improve prediction accuracy for firms without historical emission disclosure data. The largest gain comes from Linear Random Forest model. In contrast, past emission data is the best predictor for disclosing firms, and machine learning algorithms under-perform the traditional OLS estimator for these firms.