### Distributed Solar: The Democratizaton of Energy

### Blogroll

- Earle Wilson
- Lenny Smith's CHAOS: A VERY SHORT INTRODUCTION
- Subsidies for wind and solar versus subsidies for fossil fuels
- Hermann Scheer
- Healthy Home Healthy Planet
- Tony Seba
- Beautiful Weeds of New York City
- Label Noise
- John Kruschke's "Dong Bayesian data analysis" blog
- Fear and Loathing in Data Science

### climate change

- World Weather Attribution
- Steve Easterbrook's excellent climate blog: See his "The Internet: Saving Civilization or Trashing the Planet?" for example
- Nick Bower's "Scared Scientists"
- Tamino's Open Mind
- `The unchained goddess'
- Jacobson WWS literature index
- Model state level energy policy for New Englad
- Climate impacts on retail and supply chains
- Ricky Rood's “What would happen to climate if we (suddenly) stopped emitting GHGs today?
- AIP's history of global warming science: impacts

### Archives

### Jan Galkowski

# Category Archives: hierarchical clustering

## 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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## A look at an electricity consumption series using SNCDs for clustering

(Slightly amended with code and data link, 12th January 2019.) Prediction of electrical load demand or, in other words, electrical energy consumption is important for the proper operation of electrical grids, at all scales. RTOs and ISOs forecast demand based … Continue reading

Posted in American Statistical Association, consumption, data streams, decentralized electric power generation, dendrogram, divergence measures, efficiency, electricity, electricity markets, energy efficiency, energy utilities, ensembles, evidence, forecasting, grid defection, hierarchical clustering, hydrology, ILSR, information theoretic statistics, local self reliance, Massachusetts, microgrids, NCD, normalized compression divergence, numerical software, open data, prediction, rate of return regulation, Sankey diagram, SNCD, statistical dependence, statistical series, statistics, sustainability, symmetric normalized compression divergence, time series
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