Micro Cogeneration: Towards Decentralized Energy Systems
by Martin Pehnt, Martin Cames, Corinna Fischer, Barbara Praetorius, Lambert Schneider, Katja Schumacher, and Jan-Peter Vob, 2006
This book describes efforts to improve the adoption of small-scale cogeneration, or combined heat and power plants. I wrote a bit about CHP plants here.
This book is written for the German market, but describes the situation in the US, Europe, and Japan as well.
I didn’t read the whole book, as many of the chapters were overly technical for my interest-level. I’m mostly interested in the economic situation of CHP plants. Here are the chapters I read:
2. Dynamics of Socio-Technical Change: Micro Cogeneration in Energy System Transformation Scenarios
3. The Future Heating Market and the Potential for Micro Cogeneration
4. Economics of Micro Cogeneration
9. Embedding Micro Cogeneration in the Energy Supply System
11. Micro Cogeneration in North America
15. Summary and Conclusions
I think this quote sums up the difficulty of embracing decentralized CHP well:
Micro cogeneration… faces a selection environment that is geared towards central generation and long-distance transmission of electricity combined with separate heat production. The existing “regime” of energy provision may indeed represent a fundamental barrier for the widespread application of micro cogeneration technology, because it more or less subtly works towards the preservation of the existing structure: to which vested interests, actor networks, traditions, established mind sets, sunk costs, and more are attached.
My CHP project is looking at economic situations and policy levers in which utility ownership of CHP will be more favored.
Amazon Link: Micro Cogeneration: Towards Decentralized Energy Systems
Updated after rounds 1 and 2.
I have two brackets, one based on my model results below (always picking team with better total coefficient) and one based on my own picks. The one based on my own picks went 16/16 yesterday.
Maria’s own picks also went 16/16, and we did not fill out our brackets together. We do have differences in choices today, so at least one of us will not be perfect come the round of 32.
And Then There Were None
by Agatha Christie, 1939
Maria and I read Murder on the Orient Express previously and liked Agatha Christie’s storytelling. This one is a bit more haunting, but still good. There are extensive back-stories of the characters at the beginning of the book. I tried to look up these back-stories online as a reminder, but accidentally saw some spoilers. I tried not to read them, but I thought that I saw who the murderer was. Despite this, the story is so deceptive that I wasn’t really sure what was happening until the last pages of the book. Recommended.
Amazon Link: And Then There Were None by Agatha Christie
Similar to last year and the year before, I will run my NFL model over NCAA men’s basketball teams to show the strength of each team. The model predicts how many more points a team will score than the “average” team, along with how many more points they will give up on defense. The model is only based on the final scores of games during the season. Positive numbers are good for Offensive strength. Negative numbers are good for defensive strength. My model does not discount (late season runs matter no more than early season victories) and does not account for injuries.
Some things to note:
1. 8 tournament teams are worse than an NCAA Division 1 average team.
2. The top 12 teams are within 5 expected points of each other.
3. West Virginia is the 3rd best team (2nd year in a row) and got a 4 seed. Wichita State is the 11th best team and got a 10 seed.
4. The best teams not to make the tournament are Indiana (14.2 points above average, should be an 8 seed), TCU (13.5), Syracuse (13.0), and Texas Tech (12.9). Illinois State, who went 27-6, is only the 13th best team not to make the tournament (10.1).
5. Wichita State, Oklahoma State, West Virginia, Wisconsin, Marquette, Wake Forest, and Kansas State all deserved seeds that were at least 3 better.
6. Maryland, Minnesota, Arizona, Dayton, and Butler all deserved seeds that were at least 3 worse.
The Power of Habit: Why We Do What We Do in Life and Business
by Charles Duhigg, 2012
Good book about diagnosing and changing your bad habit and developing good habits. Most of what we do is habitual and done without thought. We get into a cycle where a cue triggers a routine in order to get some reward. If you have a list of websites you traverse whenever you start browsing the internet, you know this cycle. In order to fix bad habits or develop good habits, we need to understand the components of the cycle and learn how to be proactive in shaping them.
This book starts with personal habits and moves to organizational habits, with multiple examples from business. To get a flavor of the book, check out this appendix which walks through changing a single habit of the author: going to the cafeteria at work to get a cookie each afternoon.
Amazon Link: The Power of Habit: Why We Do What We Do in Life and Business
Google buys Kaggle.
Mark Cuban takes a stab at healthcare reform.
Each bitcoin transaction represented at least 26 kWh of electricity.
Between my optimistic lower-bound estimate, and the BECI, we’re still left with a staggering amount of electricity embodied in each bitcoin transaction—anywhere from 26 to 100+ kWh, or enough to power 0.9 to 3.6 US households for a day. To repeat, this is thousands of times more energy-intensive than an estimate for a credit card transaction.
My NFL betting model had a bad year. Apparently, so did sportsbooks in Las Vegas.
NFL favorites went 135-126 against the spread this past season, not counting pushes. It is the third-best mark for favorites in the last 14 seasons. And 141 games went over the total, with 124 staying under, the third-best mark for overs in the last 14 years, according to sports betting analytics site Betlabsports.com on Sports Insights. The betting majority almost always gravitates to the favorite and over, which makes it a bad combination for the books.
To boot, the New England Patriots, the consensus Super Bowl favorites throughout the season and a public favorite, went 16-3 ATS, tying the 1989 San Francisco 49ers for the best single-season ATS record in the last 40 years. In contrast, the Cleveland Browns, widely considered the biggest long shots in the league and the only team to be underdogs in every game, went a league-worst 3-12-1 ATS.
My model tends to choose underdogs due to the regularization of offense and defense strengths. Basically, I assume regression to the mean, and there was no such regression for the Patriots, Browns, Rams, or 49ers this year.
My record in games involving the Patriots: 5-12-1 (the third number is pushes or ties against the spread)
My record in games involving the Browns: 5-9-1
My record in games involving the Patriots: 4-9-2
My record in games involving the Patriots: 5-9-1
Overall in games involving those four teams: 19-36-4 (they played each other in 4 games after week 1).
“You want to gamble against the public, but at some point you’d like to see a team cover. It’s hard to be that bad and not cover, because the spreads get so inflated.”
“I would say 100 percent [the sharps] struggled,” Bogdanovich said.
“The average margin of victory in 2016 was the lowest in history, relative to total points per game.”