Distributed Solar: The Democratizaton of Energy
Blogroll
- London Review of Books
- Why It’s So Freaking Hard To Make A Good COVID-19 Model Five Thirty Eight’s take on why pandemic modeling is so difficult
- All about Sankey diagrams
- Lenny Smith's CHAOS: A VERY SHORT INTRODUCTION This is a PDF version of Lenny Smith’s book of the same title, also available from Amazon.com
- Dr James Spall's SPSA
- Mark Berliner's video lecture "Bayesian mechanistic-statistical modeling with examples in geophysical settings"
- Simon Wood's must-read paper on dynamic modeling of complex systems I highlighted Professor Wood’s paper in https://hypergeometric.wordpress.com/2014/12/26/struggling-with-problems-already-attacked/
- What If
- Comprehensive Guide to Bayes Rule
- Mike Bloomberg, 2020 He can get progress on climate done, has the means and experts to counter the Trump and Republican digital disinformation machine, and has the experience, knowledge, and depth of experience to achieve and unify.
- Giant vertical monopolies for energy have stopped making sense
- Hermann Scheer Hermann Scheer was a visionary, a major guy, who thought deep thoughts about energy, and its implications for humanity’s relationship with physical reality
- Risk and Well-Being
- The Mermaid's Tale A conversation about biological complexity and evolution, and the societal aspects of science
- Woods Hole Oceanographic Institution (WHOI)
- Nadler Strategy, LLC, on sustainability Thinking about business, efficient and effective management, and business value
- distributed solar and matching location to need
- American Association for the Advancement of Science (AAAS)
- Team Andrew Weinberg Walking September 8th for the Jimmy Fund!
- Mrooijer's Numbers R 4Us
- Ted Dunning
- South Shore Recycling Cooperative Materials management, technical assistance and networking, town advocacy, public outreach
- The Keeling Curve: its history History of the Keeling Curve and Charles David Keeling
- Earth Family Beta MIchael Osborne’s blog on Science and the like
- Healthy Home Healthy Planet
- Rasmus Bååth's Research Blog Bayesian statistics and data analysis
- The Alliance for Securing Democracy dashboard
- AP Statistics: Sampling, by Michael Porinchak Twin City Schools
- Quotes by Nikola Tesla Quotes by Nikola Tesla, including some of others he greatly liked.
- Earth Family Alpha Michael Osborne’s blog (former Executive at Austin Energy, now Chairman of the Electric Utility Commission for Austin, Texas)
- WEAPONS OF MATH DESTRUCTION Cathy O’Neil’s WEAPONS OF MATH DESTRUCTION,
- Harvard's Project Implicit
- Gabriel's staircase
- Fear and Loathing in Data Science Cory Lesmeister’s savage journey to the heart of Big Data
- Brian McGill's Dynamic Ecology blog Quantitative biology with pithy insights regarding applications of statistical methods
- Busting Myths About Heat Pumps Heat pumps are perhaps the most efficient heating and cooling systems available. Recent literature distributed by utilities hawking natural gas and other sources use performance figures from heat pumps as they were available 15 years ago. See today’s.
- Peter Congdon's Bayesian statistical modeling Peter Congdon’s collection of links pertaining to his several books on Bayesian modeling
- James' Empty Blog
- Darren Wilkinson's introduction to ABC Darren Wilkinson’s introduction to approximate Bayesian computation (“ABC”). See also his post about summary statistics for ABC https://darrenjw.wordpress.com/2013/09/01/summary-stats-for-abc/
- "Consider a Flat Pond" Invited talk introducing systems thinking, by Jan Galkowski, at First Parish in Needham, UU, via Zoom
- Leadership lessons from Lao Tzu
- WEAPONS OF MATH DESTRUCTION, reviews Reviews of Cathy O’Neil’s new book
- Ives and Dakos techniques for regime changes in series
- Prediction vs Forecasting: Knaub “Unfortunately, ‘prediction,’ such as used in model-based survey estimation, is a term that is often subsumed under the term ‘forecasting,’ but here we show why it is important not to confuse these two terms.”
- ggplot2 and ggfortify Plotting State Space Time Series with ggplot2 and ggfortify
- Earle Wilson
- "Perpetual Ocean" from NASA GSFC
- BioPython A collection of Python tools for quantitative Biology
- John Kruschke's "Dong Bayesian data analysis" blog Expanding and enhancing John’s book of same title (now in second edition!)
- OOI Data Nuggets OOI Ocean Data Lab: The Data Nuggets
climate change
- Agendaists Eli Rabett’s coining of a phrase
- The Sunlight Economy
- Climate at a glance Current state of the climate, from NOAA
- Thriving on Low Carbon
- Climate impacts on retail and supply chains
- AIP's history of global warming science: impacts The American Institute of Physics has a fine history of the science of climate change. This link summarizes the history of impacts of climate change.
- "Climate science is setttled enough"
- The great Michael Osborne's latest opinions Michael Osborne is a genius operative and champion of solar energy. I have learned never to disregard ANYTHING he says. He is mentor of Karl Ragabo, and the genius instigator of the Texas renewable energy miracle.
- Mathematics and Climate Research Network The Mathematics and Climate Research Network (MCRN) engages mathematicians to collaborating on the cryosphere, conceptual model validation, data assimilation, the electric grid, food systems, nonsmooth systems, paleoclimate, resilience, tipping points.
- The Keeling Curve The first, and one of the best programs for creating a spatially significant long term time series of atmospheric concentrations of CO2. Started amongst great obstacles by one, smart determined guy, Charles David Keeling.
- Updating the Climate Science: What path is the real world following? From Professors Makiko Sato & James Hansen of Columbia University
- Rabett Run Incisive analysis of climate science versus deliberate distraction
- Ray Pierrehumbert's site related to "Principles of Planetary Climate" THE book on climate science
- Sea Change Boston
- “Ways to [try to] slow the Solar Century''
- HotWhopper: It's excellent. Global warming and climate change. Eavesdropping on the deniosphere, its weird pseudo-science and crazy conspiracy whoppers.
- Ricky Rood's “What would happen to climate if we (suddenly) stopped emitting GHGs today?
- Climate model projections versus observations
- Energy payback period for solar panels Considering everything, how long do solar panels have to operate to offset the energy used to produce them?
- "Warming Slowdown?" (part 2 of 2) The idea of a global warming slowdown or hiatus is critically examined, emphasizing the literature, the datasets, and means and methods for telling such. The second part.
- Dessler's 6 minute Greenhouse Effect video
- On Thomas Edison and Solar Electric Power
- Earth System Models
- Spectra Energy exposed
- Reanalyses.org
- RealClimate
- Nick Bower's "Scared Scientists"
- Klaus Lackner (ASU), Silicon Kingdom Holdings (SKH) Capturing CO2 from air at scale
- Climate Change Reports By John and Mel Harte
- Ice and Snow
- SOLAR PRODUCTION at Westwood Statistical Studios Generation charts for our home in Westwood, MA
- "Getting to the Energy Future We Want," Dr Steven Chu
- Transitioning to fully renewable energy Professor Saul Griffiths talks to transitioning the customer journey, from a dependency upon fossil fuels to an electrified future
- SolarLove
- Jacobson WWS literature index
- Professor Robert Strom's compendium of resources on climate change Truly excellent
- NOAA Annual Greenhouse Gas Index report The annual assessment by the National Oceanic and Atmospheric Administration of the radiative forcing from constituent atmospheric greenhouse gases
- World Weather Attribution
- Bloomberg interactive graph on “What's warming the world''
- And Then There's Physics
- Simple box models and climate forcing IMO one of Tamino’s best posts illustrating climate forcing using simple box models
- Model state level energy policy for New Englad Bob Massie’s proposed energy policy for Massachusetts, an admirable model for energy policy anywhere in New England
- ATTP summarizes all that stuff about Committed Warming from AND THEN THERE’S PHYSICS
- Warming slowdown discussion
- Climate Change Denying Organizations
- weather blocking patterns
- Grid parity map for Solar PV in United States
- Climate Communication Hassol, Somerville, Melillo, and Hussin site communicating climate to the public
- The Carbon Cycle The Carbon Cycle, monitored by The Carbon Project
- Berkeley Earth Surface Temperature
Archives
Jan Galkowski
Category Archives: regression
Phase Plane plots of COVID-19 deaths with uncertainties
I. Introduction. It’s time to fulfill the promise made in “Phase plane plots of COVID-19 deaths“, a blog post from 2nd May 2020, and produce the same with uncertainty clouds about the functional trajectories(*). To begin, here are some assumptions … Continue reading
Posted in American Statistical Association, Andrew Harvey, anomaly detection, count data regression, COVID-19, dependent data, dlm package, Durbin and Koopman, dynamic linear models, epidemiology, filtering, forecasting, Kalman filter, LaTeX, model-free forecasting, Monte Carlo Statistical Methods, numerical algorithms, numerical linear algebra, population biology, population dynamics, prediction, R, R statistical programming language, regression, statistical learning, stochastic algorithms
Tagged prediction intervals
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Calculating Derivatives from Random Forests
(Comment on prediction intervals for random forests, and links to a paper.) (Edits to repair smudges, 2020-06-28, about 0945 EDT. Closing comment, 2020-06-30, 1450 EDT.) There are lots of ways of learning about mathematical constructs, even about actual machines. One … Continue reading
Posted in bridge to somewhere, Calculus, dependent data, dynamic generalized linear models, dynamical systems, ensemble methods, ensemble models, filtering, forecasting, hierarchical clustering, linear regression, model-free forecasting, Monte Carlo Statistical Methods, non-mechanistic modeling, non-parametric model, non-parametric statistics, numerical algorithms, prediction, R statistical programming language, random forests, regression, sampling, splines, statistical learning, statistical series, statistics, time derivatives, time series
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New COVID-19 incidence in the United States as AR(1) processes
There are several sources of information regarding Covid-19 incidence now available. This post uses data from a single source: the COVID Tracking Project. In particular I restrict attention to cumulative daily case counts for the United States, the UK, and … Continue reading
Posted in coronavirus, COVID-19, epidemiology, pandemic, regression, SARS-CoV-2
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ecoquant
