### Distributed Solar: The Democratizaton of Energy

### Blogroll

- 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.
- Team Andrew Weinberg Walking September 8th for the Jimmy Fund!
- ggplot2 and ggfortify Plotting State Space Time Series with ggplot2 and ggfortify
- Musings on Quantitative Paleoecology Quantitative methods and palaeoenvironments.
- Gabriel's staircase
- Charlie Kufs' "Stats With Cats" blog “You took Statistics 101. Now what?”
- "Perpetual Ocean" from NASA GSFC
- 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
- Peter Congdon's Bayesian statistical modeling Peter Congdon’s collection of links pertaining to his several books on Bayesian modeling
- Dr James Spall's SPSA
- 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.”
- 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”
- Woods Hole Oceanographic Institution (WHOI)
- Awkward Botany
- Quotes by Nikola Tesla Quotes by Nikola Tesla, including some of others he greatly liked.
- John Kruschke's "Dong Bayesian data analysis" blog Expanding and enhancing John’s book of same title (now in second edition!)
- 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
- Fear and Loathing in Data Science Cory Lesmeister’s savage journey to the heart of Big Data
- The Keeling Curve: its history History of the Keeling Curve and Charles David Keeling
- Mrooijer's Numbers R 4Us
- Ted Dunning
- Earle Wilson
- WEAPONS OF MATH DESTRUCTION Cathy O’Neil’s WEAPONS OF MATH DESTRUCTION,
- The Plastic Pick-Up: Discovering new sources of marine plastic pollution
- What If
- 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.
- All about ENSO, and lunar tides (Paul Pukite) Historically, ENSO has been explained in terms of winds. But recently — and Dr Paul Pukite has insisted upon this for a long time — the oscillation of ENSO has been explained as a large-scale slosh due to lunar tidal forcing.
- American Statistical Association
- Brendon Brewer on Overfitting Important and insightful presentation by Brendon Brewer on overfitting
- GeoEnergy Math Prof Paul Pukite’s Web site devoted to energy derived from geological and geophysical processes and categorized according to its originating source.
- NCAR AtmosNews
- 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
- American Association for the Advancement of Science (AAAS)
- Rasmus Bååth's Research Blog Bayesian statistics and data analysis
- Harvard's Project Implicit
- Mertonian norms
- Los Alamos Center for Bayesian Methods
- Beautiful Weeds of New York City
- "Talking Politics" podcast David Runciman, Helen Thompson
- Nadler Strategy, LLC, on sustainability Thinking about business, efficient and effective management, and business value
- Bob Altemeyer on authoritarianism (via Dan Satterfield) The science behind the GOP civil war
- Subsidies for wind and solar versus subsidies for fossil fuels
- 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.
- Tony Seba Solar energy, electric vehicle, energy storage, and business disruption professor and visionary
- Gavin Simpson
- distributed solar and matching location to need
- WEAPONS OF MATH DESTRUCTION, reviews Reviews of Cathy O’Neil’s new book
- Flettner Rotor Bruce Yeany introduces the Flettner Rotor and related science
- Leverhulme Centre for Climate Change Mitigation
- Brian McGill's Dynamic Ecology blog Quantitative biology with pithy insights regarding applications of statistical methods

### climate change

- Ray Pierrehumbert's site related to "Principles of Planetary Climate" THE book on climate science
- Ellenbogen: There is no Such Thing as Wind Turbine Syndrome
- 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.
- Grid parity map for Solar PV in United States
- HotWhopper: It's excellent. Global warming and climate change. Eavesdropping on the deniosphere, its weird pseudo-science and crazy conspiracy whoppers.
- ATTP summarizes all that stuff about Committed Warming from AND THEN THERE’S PHYSICS
- Ricky Rood's “What would happen to climate if we (suddenly) stopped emitting GHGs today?
- Climate model projections versus observations
- 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
- "A field guide to the climate clowns"
- The HUMAN-caused greenhouse effect, in under 5 minutes, by Bill Nye
- Thriving on Low Carbon
- "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.
- Climate impacts on retail and supply chains
- Solar Gardens Community Power
- Agendaists Eli Rabett’s coining of a phrase
- "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.
- Klaus Lackner (ASU), Silicon Kingdom Holdings (SKH) Capturing CO2 from air at scale
- James Powell on sampling the climate consensus
- 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.
- Interview with Wally Broecker Interview with Wally Broecker
- "Mighty Microgrids" Webinar This is a Webinar on YouTube about Microgrids from the Institute for Local Self-Reliance (ILSR), featuring New York State and Minnesota
- 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
- Professor Robert Strom's compendium of resources on climate change Truly excellent
- 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.
- "Lessons of the Little Ice Age" (Farber) From Dan Farber, at LEGAL PLANET
- Simple box models and climate forcing IMO one of Tamino’s best posts illustrating climate forcing using simple box models
- Updating the Climate Science: What path is the real world following? From Professors Makiko Sato & James Hansen of Columbia University
- Exxon-Mobil statement on UNFCCC COP21
- The Green Plate Effect Eli Rabett’s “The Green Plate Effect”
- Climate Communication Hassol, Somerville, Melillo, and Hussin site communicating climate to the public
- Tuft's Professor Kenneth Lang on the physical chemistry of the Greenhouse Effect
- “Ways to [try to] slow the Solar Century''
- "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.
- Tamino's Open Mind Open Mind: A statistical look at climate, its science, and at science denial
- “The discovery of global warming'' (American Institute of Physics)
- Warming slowdown discussion
- 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
- History of discovering Global Warming From the American Institute of Physics.
- Andy Zucker's "Climate Change and Psychology"
- Transitioning to fully renewable energy Professor Saul Griffiths talks to transitioning the customer journey, from a dependency upon fossil fuels to an electrified future
- Wally Broecker on climate realism
- Eli on the spectroscopic basis of atmospheric radiation physical chemistry
- "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.)
- Anti—Anti-#ClimateEmergency Whether to declare a climate emergency is debatable. But some critics have gone way overboard.
- Nick Bower's "Scared Scientists"
- Non-linear feedbacks in climate (discussion of Bloch-Johnson, Pierrehumbert, Abbot paper) Discussion of http://onlinelibrary.wiley.com/wol1/doi/10.1002/2015GL064240/abstract
- Rabett Run Incisive analysis of climate science versus deliberate distraction
- The Scientific Case for Modern Human-caused Global Warming
- 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.

### Archives

### Jan Galkowski

# Category Archives: dynamic linear models

## 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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## Stream flow and P-splines: Using built-in estimates for smoothing

Mother Brook in Dedham Massachusetts was the first man-made canal in the United States. Dug in 1639, it connects the Charles River at Dedham, to the Neponset River in the Hyde Park section of Boston. It was originally an important … Continue reading

Posted in American Statistical Association, citizen data, citizen science, Clausius-Clapeyron equation, Commonwealth of Massachusetts, cross-validation, data science, dependent data, descriptive statistics, dynamic linear models, empirical likelihood, environment, flooding, floods, Grant Foster, hydrology, likelihood-free, meteorological models, model-free forecasting, non-mechanistic modeling, non-parametric, non-parametric model, non-parametric statistics, numerical algorithms, precipitation, quantitative ecology, statistical dependence, statistical series, stream flow, Tamino, the bootstrap, time series, water vapor
2 Comments

## climate model democracy

“One of the most interesting things about the MIP ensembles is that the mean of all the models generally has higher skill than any individual model.” We hold these truths to be self-evident, that all models are created equal, that … Continue reading

Posted in American Association for the Advancement of Science, American Meteorological Association, American Statistical Association, AMETSOC, Anthropocene, attribution, Bayesian model averaging, Bloomberg, citizen science, climate, climate business, climate change, climate data, climate disruption, climate education, climate justice, Climate Lab Book, climate models, coastal communities, coastal investment risks, complex systems, differential equations, disruption, dynamic linear models, dynamical systems, ecology, emergent organization, ensemble methods, ensemble models, ensembles, Eric Rignot, evidence, fear uncertainty and doubt, FEMA, forecasting, free flow of labor, global warming, greenhouse gases, greenwashing, Humans have a lot to answer for, Hyper Anthropocene, Jennifer Francis, Joe Romm, Kevin Anderson, Lévy flights, LBNL, leaving fossil fuels in the ground, liberal climate deniers, mathematics, mathematics education, model-free forecasting, multivariate adaptive regression splines, National Center for Atmospheric Research, obfuscating data, oceanography, open source scientific software, optimization, perceptrons, philosophy of science, phytoplankton
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## When linear systems can’t be solved by linear means

Linear systems of equations and their solution form the cornerstone of much Engineering and Science. Linear algebra is a paragon of Mathematics in the sense that its theory is what mathematicians try to emulate when they develop theory for many … Continue reading

## on nonlinear dynamics of hordes of people

I spent a bit of last week at a symposium honoring the work of Charney and Lorenz in fluid dynamics. I am no serious student of fluid dynamics. I have a friend, Klaus, an engineer, who is, and makes a … Continue reading

Posted in Anthropocene, bifurcations, biology, Carl Safina, causation, complex systems, dynamic generalized linear models, dynamic linear models, dynamical systems, ecological services, ecology, Emily Shuckburgh, finance, Floris Takens, fluid dynamics, fluid eddies, games of chance, Hyper Anthropocene, investments, Lenny Smith, Lorenz, nonlinear, numerical algorithms, numerical analysis, politics, population biology, population dynamics, prediction markets, Principles of Planetary Climate, public transport, Ray Pierrehumbert, risk, sampling networks, sustainability, Timothy Lenton, Yale University Statistics Department, zero carbon, ``The tide is risin'/And so are we''
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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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## 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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## Six cases of models

The previous post included an attempt to explain land surface temperatures as estimated by the BEST project using a dynamic linear model including regressions on both quarterly CO2 concentrations and ocean heat content. The idea was to check the explanatory … Continue reading

Posted in AMETSOC, anemic data, Anthropocene, astrophysics, Bayesian, Berkeley Earth Surface Temperature project, BEST, carbon dioxide, climate, climate change, climate data, climate disruption, climate models, dlm package, dynamic linear models, dynamical systems, environment, fossil fuels, geophysics, Giovanni Petris, global warming, greenhouse gases, Hyper Anthropocene, information theoretic statistics, maths, maximum likelihood, meteorology, model comparison, numerical software, Patrizia Campagnoli, Rauch-Tung-Striebel, Sonia Petrone, state-space models, stochastic algorithms, stochastic search, SVD, time series
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## “The Myth of the 1970s Global Cooling Consensus”

“The Myth of the 1970s Global Cooling Scientific Consensus“, T. C. Peterson, W. M. Connolley, J. Fleck, http://dx.doi.org/10.1175/2008BAMS2370.1. Abstract Climate science as we know it today did not exist in the 1960s and 1970s. The integrated enterprise embodied in the … Continue reading

Posted in AMETSOC, Anthropocene, carbon dioxide, citizen science, climate, climate change, climate data, climate disruption, climate education, climate zombies, coastal communities, differential equations, dynamic linear models, dynamical systems, ecology, environment, fluid dynamics, fossil fuel divestment, fossil fuels, geophysics, global warming, greenhouse gases, Hyper Anthropocene, ice sheet dynamics, investing
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## Phytoplankton-delineated oceanic eddies near Antarctica

Excerpt, from NASA: Phytoplankton are the grass of the sea. They are floating, drifting, plant-like organisms that harness the energy of the Sun, mix it with carbon dioxide that they take from the atmosphere, and turn it into carbohydrates and … Continue reading

Posted in AMETSOC, Antarctica, Arctic, bacteria, Carbon Cycle, complex systems, differential equations, diffusion, diffusion processes, dynamic linear models, dynamical systems, Emily Shuckburgh, environment, fluid dynamics, geophysics, GLMs, John Marshall, marine biology, Mathematics and Climate Research Network, NASA, numerical analysis, numerical software, oceanic eddies, oceanography, physics, phytoplankton, science, thermohaline circulation, WHOI, Woods Hole Oceanographic Institution
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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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## “The storage necessity myth: how to choreograph high-renewables electricity systems”

(This was originally presented by CleanTechMedia.) Sounds like a great role for smart control systems. Flash COP21 won’t matter. Listen to Professor Tony Seba. (Use your browser Back button to return to this blog.) Excerpt: Clearly, though, many vested interests … Continue reading

Posted in adaptation, Anthropocene, Cape Wind, Carbon Tax, citizenship, clean disruption, climate change, climate disruption, conservation, consumption, decentralized electric power generation, decentralized energy, demand-side solutions, denial, dynamic linear models, dynamical systems, economics, efficiency, energy, energy reduction, energy utilities, engineering, fear uncertainty and doubt, forecasting, fossil fuel divestment, fossil fuels, global warming, Hyper Anthropocene, ignorance, investment in wind and solar energy, meteorology, microgrids, natural gas, obfuscating data, planning, politics, public utility commissions, PUCs, rationality, reasonableness, Sankey diagram, solar energy, solar power, SolarPV.tv, Stanford University, sustainability, the right to know, Tony Seba, University of California Berkeley, wind energy, wind power, zero carbon
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## Thoughts on “Regime Shift?”

John Baez at The Azimuth Project opened a discussion on the recent paper by Reid, et al Philip C. Reid et al, Global impacts of the 1980s regime shift on the Earth’s climate and systems, Global Change Biology, 2015. I … Continue reading

## Is Earth Much More Sensitive to CO2 Than Thought?

Originally posted on Climate Denial Crock of the Week:

A nahcolite from the Eocene Green River Formation. Credit: Timothy Lowenstein Phys.org: Ancient climates on Earth may have been more sensitive to carbon dioxide than was previously thought, according to new…

Posted in Anthropocene, Carbon Cycle, carbon dioxide, climate, climate change, climate data, climate disruption, differential equations, diffusion processes, dynamic linear models, dynamical systems, environment, fossil fuels, generalized linear models, geophysics, global warming, Hyper Anthropocene, Principles of Planetary Climate, risk, science
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## Southern Oscillation (SOI) correlated with Outgoing Longwave Radiation (OLR)

To the climate community this is nothing at all new, but I spotted these time series today and thought they would make a nice exhibit on how something people have direct control over, greenhouse gas emissions, affect a “teleconnection mechanism” … Continue reading

Posted in AMETSOC, bifurcations, carbon dioxide, climate, climate change, climate disruption, climate models, Dan Satterfield, differential equations, dynamic linear models, dynamical systems, ENSO, environment, forecasting, generalized linear models, geophysics, global warming, greenhouse gases, IPCC, Mathematica, mathematics, maths, meteorology, NCAR, NOAA, numerical software, oceanography, open data, physics, population biology, Principles of Planetary Climate, rationality, reasonableness, science, Spaceship Earth, state-space models, thermodynamics, time series
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## On differential localization of tumors using relative concentrations of ctDNA. Part 1.

Like most mammalian tissue, tumors often produce shards of DNA as a byproduct of cell death and fracture. This circulating tumor DNA is being studied as a means of detecting tumors or their resurgence after treatment. (See also a Q&A … Continue reading

Posted in approximate Bayesian computation, Bayesian, Bayesian inversion, cardiovascular system, diffusion, dynamic linear models, eigenanalysis, engineering, forecasting, mathematics, maths, medicine, networks, prediction, spatial statistics, statistics, stochastic algorithms, stochastic search, wave equations
3 Comments

## On Changing Things

You never change things by fighting the existing reality. To change something, build a new model that makes the existing model obsolete. That’s from Buckminster Fuller, a fellow Unitarian.

Posted in adaptation, Anthropocene, bifurcations, bridge to nowhere, Buckminster Fuller, Cauchy distribution, clean disruption, climate disruption, demand-side solutions, destructive economic development, Disney, dynamic linear models, dynamical systems, Epcot, exponential growth, fossil fuel divestment, global warming, Hyper Anthropocene, physical materialism, planning, rationality, reasonableness, Spaceship Earth, stochastic algorithms
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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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## Utilities for dummies: How they work and why that needs to change (from grist.org)

“Utilities are shielded by a force field of tedium.” “Solar panels could destroy U.S. utilities, according to U.S. utilities.” Utilities for dummies: How they work and why that needs to change“, a compact introduction, from grist.org. And there’s an additional … Continue reading

Posted in Anthropocene, bifurcations, bridge to nowhere, carbon dioxide, citizenship, civilization, clean disruption, climate, climate change, climate disruption, climate education, conservation, consumption, corruption, decentralized electric power generation, decentralized energy, demand-side solutions, destructive economic development, disingenuity, dynamic linear models, dynamical systems, ecology, economics, education, efficiency, energy, energy reduction, energy utilities, engineering, environment, ethics, exponential growth, finance, forecasting, fossil fuel divestment, fossil fuels, fracking, global warming, Hyper Anthropocene, ignorance, investing, investment in wind and solar energy, mathematics, maths, meteorology, methane, microgrids, natural gas, optimization, physics, pipelines, politics, prediction, public utility commissions, PUCs, rationality, reasonableness, risk, science, solar power, statistics, sustainability, taxes, temporal myopia, the right to know, time series, Tony Seba, wind power, zero carbon
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## Solar installation progress, courtesy of MacSolarIndex.com

The MAC Solar Index tracks a set of solar manufacturing and installation companies. It is also the basis for the Guggenheim Investments “TAN” Exchange-Traded Fund (“ETF”, *). They recently published a progress report on global solar installations, which I wanted … Continue reading

Posted in adaptation, Anthropocene, carbon dioxide, citizen science, citizenship, civilization, clean disruption, climate, climate change, climate data, climate disruption, conservation, consumption, decentralized electric power generation, decentralized energy, demand-side solutions, destructive economic development, dynamic linear models, dynamical systems, ecology, economics, efficiency, energy, energy reduction, environment, exponential growth, forecasting, fossil fuel divestment, fossil fuels, geophysics, global warming, Hyper Anthropocene, investing, investment in wind and solar energy, mathematics, mathematics education, maths, meteorology, microgrids, open data, optimization, physics, politics, prediction, rationality, reasonableness, risk, science, science education, solar power, sustainability, the right to know, time series, Tony Seba, wind power, zero carbon
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## SCIENCE OF DOOM takes on assessing zero Carbon power and a zero Carbon grid

Updated, 2127 EDT, 10th August 2015 The blog, Science of Doom, has taken on a new thread discussing the technical feasibilities and problems associated with building out zero Carbon energy in the context of an electric grid. As such, it … Continue reading

Posted in adaptation, Anthropocene, clean disruption, climate data, climate disruption, conservation, consumption, decentralized electric power generation, decentralized energy, demand-side solutions, destructive economic development, dynamic linear models, dynamical systems, economics, efficiency, energy, energy reduction, engineering, environment, exponential growth, forecasting, fossil fuel divestment, global warming, Hyper Anthropocene, investing, investment in wind and solar energy, microgrids, open data, optimization, prediction, rationality, reasonableness, risk, solar power, state-space models, stochastics, sustainability, the right to know, time series, wind power, Wordpress, zero carbon
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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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