Distributed Solar: The Democratizaton of Energy
Blogroll
- John Cook's reasons to use Bayesian inference
- Logistic curves in market disruption From DollarsPerBBL, about logistic or S-curves as models of product take-up rather than exponentials, with notes on EVs
- The Keeling Curve: its history History of the Keeling Curve and Charles David Keeling
- Flettner Rotor Bruce Yeany introduces the Flettner Rotor and related science
- Why "naive Bayes" is not Bayesian Explains why the so-called “naive Bayes” classifier is not Bayesian. The setup is okay, but estimating probabilities by doing relative frequencies instead of using Dirichlet conjugate priors or integration strays from The Path.
- "The Expert"
- London Review of Books
- Professor David Draper
- 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
- American Statistical Association
- WEAPONS OF MATH DESTRUCTION, reviews Reviews of Cathy O’Neil’s new book
- Slice Sampling
- International Society for Bayesian Analysis (ISBA)
- "Impacts of Green New Deal energy plans on grid stability, costs, jobs, health, and climate in 143 countries" (Jacobson, Delucchi, Cameron, et al) Global warming, air pollution, and energy insecurity are three of the greatest problems facing humanity. To address these problems, we develop Green New Deal energy roadmaps for 143 countries.
- Ted Dunning
- Pat's blog While it is described as “The mathematical (and other) thoughts of a (now retired) math teacher”, this is false humility, as it chronicles the present and past life and times of mathematicians in their context. Recommended.
- Brian McGill's Dynamic Ecology blog Quantitative biology with pithy insights regarding applications of statistical methods
- Label Noise
- SASB Sustainability Accounting Standards Board
- "Talking Politics" podcast David Runciman, Helen Thompson
- Awkward Botany
- Rasmus Bååth's Research Blog Bayesian statistics and data analysis
- Gabriel's staircase
- Dominic Cummings blog Chief advisor to the PM, United Kingdom
- John Kruschke's "Dong Bayesian data analysis" blog Expanding and enhancing John’s book of same title (now in second edition!)
- Carl Safina's blog One of the wisest on Earth
- 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/
- Karl Broman
- Mark Berliner's video lecture "Bayesian mechanistic-statistical modeling with examples in geophysical settings"
- Woods Hole Oceanographic Institution (WHOI)
- 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
- Healthy Home Healthy Planet
- 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.
- Harvard's Project Implicit
- All about Sankey diagrams
- Earth Family Alpha Michael Osborne’s blog (former Executive at Austin Energy, now Chairman of the Electric Utility Commission for Austin, Texas)
- Nadler Strategy, LLC, on sustainability Thinking about business, efficient and effective management, and business value
- Earth Family Beta MIchael Osborne’s blog on Science and the like
- Higgs from AIR describing NAO and EA Stephanie Higgs from AIR Worldwide gives a nice description of NAO and EA in the context of discussing “The Geographic Impact of Climate Signals on European Winter Storms”
- AP Statistics: Sampling, by Michael Porinchak Twin City Schools
- Tim Harford's “More or Less'' Tim Harford explains – and sometimes debunks – the numbers and statistics used in political debate, the news and everyday life
- Risk and Well-Being
- OOI Data Nuggets OOI Ocean Data Lab: The Data Nuggets
- BioPython A collection of Python tools for quantitative Biology
- Leverhulme Centre for Climate Change Mitigation
- Musings on Quantitative Paleoecology Quantitative methods and palaeoenvironments.
- Brendon Brewer on Overfitting Important and insightful presentation by Brendon Brewer on overfitting
- In Monte Carlo We Trust The statistics blog of Matt Asher, actually called the “Probability and Statistics Blog”, but his subtitle is much more appealing. Asher has a Manifesto at http://www.statisticsblog.com/manifesto/.
- distributed solar and matching location to need
- 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.”
climate change
- Professor Robert Strom's compendium of resources on climate change Truly excellent
- Nick Bower's "Scared Scientists"
- Mrooijer's Global Temperature Explorer
- Paul Beckwith Professor Beckwith is, in my book, one of the most insightful and analytical observers on climate I know. I highly recommend his blog, and his other informational products.
- “The Irrelevance of Saturation: Why Carbon Dioxide Matters'' (Bart Levenson)
- "When Did Global Warming Stop" Doc Snow’s treatment of the denier claim that there’s been no warming for the most recent N years. (See http://hubpages.com/@doc-snow for more on him.)
- Wally Broecker on climate realism
- Solar Gardens Community Power
- `The unchained goddess' 1958 Bell Telephone Science Hour broadcast regarding, among other things, climate change.
- Climate change: Evidence and causes A project of the UK Royal Society: (1) Answers to key questions, (2) evidence and causes, and (3) a short guide to climate science
- 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
- Social Cost of Carbon
- Documenting the Climate Deniarati at work
- 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
- 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.
- Exxon-Mobil statement on UNFCCC COP21
- The Scientific Case for Modern Human-caused Global Warming
- Climate Communication Hassol, Somerville, Melillo, and Hussin site communicating climate to the public
- The beach boondoggle Prof Rob Young on how owners of beach property are socializing their risks at costs to all of us, not the least being it seems coastal damage is less than it actually is
- Earth System Models
- ATTP summarizes all that stuff about Committed Warming from AND THEN THERE’S PHYSICS
- Steve Easterbrook's excellent climate blog: See his "The Internet: Saving Civilization or Trashing the Planet?" for example Heavy on data and computation, Easterbrook is a CS prof at UToronto, but is clearly familiar with climate science. I like his “The Internet: Saving Civilization or Trashing the Planet” very much.
- Sir David King David King’s perspective on climate, and the next thousands of years for humanity
- Klaus Lackner (ASU), Silicon Kingdom Holdings (SKH) Capturing CO2 from air at scale
- Warming slowdown discussion
- David Appell's early climate science
- SOLAR PRODUCTION at Westwood Statistical Studios Generation charts for our home in Westwood, MA
- 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 model projections versus observations
- 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.
- Thriving on Low Carbon
- Non-linear feedbacks in climate (discussion of Bloch-Johnson, Pierrehumbert, Abbot paper) Discussion of http://onlinelibrary.wiley.com/wol1/doi/10.1002/2015GL064240/abstract
- “Ways to [try to] slow the Solar Century''
- History of discovering Global Warming From the American Institute of Physics.
- "A field guide to the climate clowns"
- James Hansen and granddaughter Sophie on moving forward with progress on climate
- James Powell on sampling the climate consensus
- Ice and Snow
- And Then There's Physics
- “The discovery of global warming'' (American Institute of Physics)
- Spectra Energy exposed
- HotWhopper: It's excellent. Global warming and climate change. Eavesdropping on the deniosphere, its weird pseudo-science and crazy conspiracy whoppers.
- 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.
- Isaac Held's blog In the spirit of Ray Pierrehumbert’s “big ideas come from small models” in his textbook, PRINCIPLES OF PLANETARY CLIMATE, Dr Held presents quantitative essays regarding one feature or another of the Earth’s climate and weather system.
- Transitioning to fully renewable energy Professor Saul Griffiths talks to transitioning the customer journey, from a dependency upon fossil fuels to an electrified future
- "Warming Slowdown?" (part 1 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. In two parts.
- Ray Pierrehumbert's site related to "Principles of Planetary Climate" THE book on climate science
- Simple models of climate change
- CLIMATE ADAM Previously from the Science news staff at the podcast of Nature (“Nature Podcast”), the journal, now on YouTube, encouraging climate action through climate comedy.
- The Green Plate Effect Eli Rabett’s “The Green Plate Effect”
Archives
Jan Galkowski
Category Archives: Kalman filter
Baseload is an intellectual crutch for engineers and utility managers who cannot think dynamically
This is an awesome presentation by Professor Joshua Pearce of Michigan Technological University. (h/t Peter Sinclair’s Climate Denial Crock of the Week) The same idea, that “baseload is a shortcut for engineers who can’t think dynamically”, was similar in the … Continue reading
Posted in American Solar Energy Society, an ignorant American public, Bloomberg Green, Bloomberg New Energy Finance, bridge to somewhere, CleanTechnica, control theory, controls theory, decentralized electric power generation, decentralized energy, differential equations, dynamic linear models, dynamical systems, electrical energy engineering, electrical energy storage, electricity, Kalman filter, optimization, photovoltaics, rate of return regulation, solar domination, solar energy, solar revolution, stochastic algorithms, utility company death spiral, wind energy, wind power, zero carbon
Tagged baseload, controls theory, dynamics, electrical engineering, energy storage, marginal cost of energy, solar energy, wind energy
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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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What happens when time sampling density of a series matches its growth
This is the newly updated map of COVID-19 cases in the United States, updated, presumably, because of the new emphasis upon testing: How do we know this is the recent of recent testing? Look at the map of active cases: … Continue reading
Posted in American Association for the Advancement of Science, American Statistical Association, anti-intellectualism, anti-science, climate denial, corruption, data science, data visualization, Donald Trump, dump Trump, epidemiology, experimental science, exponential growth, forecasting, Kalman filter, model-free forecasting, nonlinear systems, open data, penalized spline regression, population dynamics, sampling algorithms, statistical ecology, statistical models, statistical regression, statistical series, statistics, sustainability, the right to know, the stack of lies
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Repaired R code for Markov spatial simulation of hurricane tracks from historical trajectories
(Slight update, 28th June 2020.) I’m currently studying random walk and diffusion processes and their connections with random fields. I’m interested in this because at the core of dynamic linear models, Kalman filters, and state-space methods there is a random … Continue reading
Posted in American Meteorological Association, American Statistical Association, AMETSOC, Arthur Charpentier, atmosphere, diffusion, diffusion processes, dynamic linear models, dynamical systems, environment, geophysics, hurricanes, Kalman filter, Kerry Emanuel, Lévy flights, Lorenz, Markov chain random fields, mathematics, mathematics education, maths, MCMC, mesh models, meteorological models, meteorology, model-free forecasting, Monte Carlo Statistical Methods, numerical analysis, numerical software, oceanography, open data, open source scientific software, physics, random walk processes, random walks, science, spatial statistics, state-space models, statistical dependence, statistics, stochastic algorithms, stochastics, time series
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A model of an electrical grid: A vision
Many people seem to view the electrical grid of the future being much like the present one. I think a lot about networks, because of my job. And I especially think a lot about network topologies, although primarily concerning the … Continue reading
Posted in abstraction, American Meteorological Association, anomaly detection, Anthropocene, Bloomberg New Energy Finance, BNEF, Boston, bridge to somewhere, Buckminster Fuller, Canettes Blues Band, clean disruption, climate business, climate economics, complex systems, corporate supply chains, decentralized electric power generation, decentralized energy, demand-side solutions, differential equations, distributed generation, efficiency, EIA, electricity, electricity markets, energy, energy reduction, energy storage, energy utilities, engineering, extended supply chains, green tech, grid defection, Hermann Scheer, Hyper Anthropocene, investment in wind and solar energy, ISO-NE, Kalman filter, kriging, Lawrence Berkeley National Laboratory, leaving fossil fuels in the ground, Lenny Smith, local generation, marginal energy sources, Massachusetts Clean Energy Center, Mathematics and Climate Research Network, mesh models, meteorology, microgrids, networks, New England, New York State, open data, organizational failures, pipelines, planning, prediction markets, public utility commissions, PUCs, rate of return regulation, rationality, reason, reasonableness, regime shifts, regulatory capture, resiliency, risk, Sankey diagram, smart data, solar domination, solar energy, solar power, Spaceship Earth, spatial statistics, state-space models, statistical dependence, statistics, stochastic algorithms, stochastics, stranded assets, supply chains, sustainability, the energy of the people, the green century, the value of financial assets, thermodynamics, time series, Tony Seba, utility company death spiral, wave equations, wind energy, wind power, zero carbon
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On Smart Data
One of the things I find surprising, if not astonishing, is that in the rush to embrace Big Data, a lot of learning and statistical technique has been left apparently discarded along the way. I’m hardly the first to point … Continue reading
Posted in Akaike Information Criterion, Bayes, Bayesian, Bayesian inversion, big data, bigmemory package for R, changepoint detection, data science, data streams, dlm package, dynamic generalized linear models, dynamic linear models, dynamical systems, Generalize Additive Models, generalized linear models, information theoretic statistics, Kalman filter, linear algebra, logistic regression, machine learning, Markov Chain Monte Carlo, mathematics, mathematics education, maths, maximum likelihood, MCMC, Monte Carlo Statistical Methods, multivariate statistics, numerical analysis, numerical software, numerics, quantitative biology, quantitative ecology, rationality, reasonableness, sampling, smart data, state-space models, statistical dependence, statistics, the right to know, time series
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Cory Lesmeister’s treatment of Simson’s Paradox (at “Fear and Loathing in Data Science”)
(Updated 2016-05-08, to provide reference for plateaus of ML functions in vicinity of MLE.) Simpson’s Paradox is one of those phenomena of data which really give Statistics a substance and a role, beyond the roles it inherits from, say, theoretical … Continue reading
Posted in Akaike Information Criterion, approximate Bayesian computation, Bayes, Bayesian, evidence, Frequentist, games of chance, information theoretic statistics, Kalman filter, likelihood-free, mathematics, maths, maximum likelihood, Monte Carlo Statistical Methods, probabilistic programming, rationality, Rauch-Tung-Striebel, Simpson's Paradox, state-space models, statistical dependence, statistics, stochastics
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Gavin Simpson updates his temperature analysis
See the very interesting discussion at his blog, From the bottom of the heap. It would be nice to see some information theoretic measures on these results, though.
Posted in AMETSOC, Anthropocene, astrophysics, Berkeley Earth Surface Temperature project, carbon dioxide, changepoint detection, climate, climate change, climate data, climate disruption, climate models, ecology, environment, evidence, Gavin Simpson, Generalize Additive Models, geophysics, global warming, HadCRUT4, hiatus, Hyper Anthropocene, information theoretic statistics, Kalman filter, maths, meteorology, numerical analysis, R, rationality, reasonableness, splines, time series
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dynamic linear model applied to sea-level-rise anomalies
I spent much of the data working up a function for level+trend dynamic linear modeling based upon the dlm package by Petris, Petrone, and Campagnoli, while trying some calculations and code for regime shift detection. One of the test cases … Continue reading
Posted in Bayesian, citizen science, climate change, climate data, climate disruption, dynamic linear models, floods, forecasting, Frequentist, global warming, icesheets, information theoretic statistics, Kalman filter, meteorology, open data, sea level rise, state-space models, statistics, time series
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Solar array with cloud predicting technology launched in WA
Australia’s first grid-connected solar power project with cloud predicting technology launched at Karratha Airport, WA, in bid to smooth solar supply. Source: Solar array with cloud predicting technology launched in WA
Posted in adaptation, Anthropocene, carbon dioxide, citizenship, civilization, clean disruption, climate, climate change, climate disruption, conservation, consumption, decentralized electric power generation, decentralized energy, demand-side solutions, dynamic linear models, efficiency, energy, energy reduction, energy utilities, engineering, environment, ethics, forecasting, geophysics, global warming, Hyper Anthropocene, investment in wind and solar energy, Kalman filter, mathematics, maths, meteorology, microgrids, mitigation, NCAR, numerical software, optimization, physics, prediction, probabilistic programming, rationality, reasonableness, risk, science, solar power, stochastics, sustainability, time series
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Comprehensive and compact tutorial on Petris’ DLM package in R; with an update about Helske’s KFAS
A blogger named Lalas produced on Quantitative Thoughts a very comprehensive and compact tutorial on the R package dlm by Petris. I use dlm a lot. Unfortunately, Lalas does not give details on how the SVD is used. They do … Continue reading
Posted in Bayes, Bayesian, dynamic linear models, dynamical systems, forecasting, Kalman filter, mathematics, maths, multivariate statistics, numerical software, open source scientific software, prediction, R, Rauch-Tung-Striebel, state-space models, statistics, stochastic algorithms, SVD, time series
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ecoquant
