Quantifying Energy Investments using Data Science

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Dr. Spyros Giannelos

5:44:44

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1 - Introduction

1 - Overview.mp4

00:37

2 - Analysis.mp4

04:58

2 - The Theory of Energy Investments

3 - Decarbonizationdriven Energy Investments.mp4

05:43

3 - slides.pdf

4 - Flexibility and Energy Investments.mp4

05:54

5 - Future Peak Load as a Signal for Investments.mp4

06:33

6 - The concept of build time of electricity infrastructure assets.mp4

01:40

7 - The concept of the economic lifetime of an electricity infrastructure asset.mp4

01:40

8 - The concept of stranding risk of electricity infrastructure investments.mp4

04:06

9 - Python Investments are in GW and not in GWh Demonstration of the differences.mp4

08:53

3 - Investment combinations

10 - Investment combinations for offshore farms.mp4

13:16

10 - possible-offshore-invesments.xlsx

4 - Analysis on Energy Investments using Python

11 - Identifying mutually exclusive investments.mp4

31:26

11 - investment-code.xlsx

12 - Grouping the mutually exclusive investment strategies.mp4

26:29

12 - investment-code.xlsx

13 - Filtering investment strategies.mp4

27:21

13 - candidate-strategies.xlsx

13 - dataset-reinforcing.xlsx

13 - scatter-plots.zip

14 - Comparison of Investment solutions.mp4

17:24

14 - investment-solutions.xlsx

5 - How the investment cost decreases with increase in deployment of technologies

15 - Introduction.mp4

04:48

16 - Producing the Scurve using different probability distributions.mp4

16:57

17 - Exponential & Sigmoid Learning curve Experience curve for novel investments.mp4

17:32

6 - Annualization of expenses

18 - Annualizing costs using excel.mp4

13:21

19 - Annualizing costs using python.mp4

01:51

20 - Capitalization factor with changing discount rates.mp4

17:52

21 - Three ways to calculate the capitalization factor.mp4

07:12

7 - Costs

22 - Fixed and variable cost calculation.mp4

05:07

23 - Per unit costs.mp4

06:50

24 - The famous Spackman method.mp4

39:09

8 - Discount Factors

25 - The concept of epoch and horizon.mp4

08:27

26 - Discount factor reading from Excel.mp4

07:48

27 - Discount factor constructing using Python.mp4

05:19

28 - Cumulative discount factor for operational costs.mp4

20:41

29 - Cumulative discount factor for investment costs.mp4

15:50

9 - Bonus

30 - Discount-Coupons-october-2022.pdf

30 - Extra.html

30 - my personal website with great offers for you.zip

Description

Theory of Energy Investments, Learning Curve, Discount Factors, Capitalization, Data Analysis on Investments

What You'll Learn?

The theory of Energy Investments
Flexibility, Drivers for Energy Investments
Investment combinations for Offshore wind farms
How the investment cost reduces with the investment amount
Capitalization of investment costs
CAPEX calculation, Fixed, Variable and per-unit costs
Discount Factors and cumulative discounting
The subtitles are manually created. Therefore, they are fully accurate. They are not auto-generated.
Part of the giannelos dot com official certificate

Who is this for?

Entrepreneurs

Economists

Quants

Members of the highly googled giannelos dot com program

Investment Bankers

Academics, PhD Students, MSc Students, Undergrads

Postgraduate and PhD students.

Data Scientists

Energy professionals (investment planning, power system analysis)

Software Engineers

Finance professionals

More details

Description
What is the course about:
This course teaches how to use Data Science for investments in energy. Specifically, we learn how to calculate costs:Â fixed investment costs, variable investment costs, capital expenditure, and per unit costs.
We then learn how to discount these costs - using discount factors and cumulative discount factors.
Then, we learn how to identify mutual investments from large investment datasets.
We accompany these Python implementations with the theory of energy investments.
We also use the Learning curve and S- curve to understand more about the behavior of investment costs - how these costs change with more investment.
This is the absolute course for economic and financial studies on investmentsÂ (quantitative and theoretical).

Who:
I am a research fellow at Imperial College London, and I have been part of high-tech projects at the intersection of Academia & Industry for over 10 years, prior to, during & after my Ph.D. I am also the founder of the giannelos dot com program in data science.
Doctor of Philosophy (Ph.D.) in Analytics & Mathematical Optimization applied to Energy Investments, from Imperial College London, and Masters of Engineering (M. Eng.) in Power Systems and Economics.
Important:
Prerequisites: The course Data Science Code that appears all the time at Workplace.
Every detail is explained, so that you won't have to search online, or guess. In the end, you will feel confident in your knowledge and skills.
We start from scratch so that you do not need to have done any preparatory work in advance at all.Â Just follow what is shown on screen, because we go slowly and explain everything in detail.
Who this course is for:
Entrepreneurs
Economists
Quants
Members of the highly googled giannelos dot com program
Investment Bankers
Academics, PhD Students, MSc Students, Undergrads
Postgraduate and PhD students.
Data Scientists
Energy professionals (investment planning, power system analysis)
Software Engineers
Finance professionals

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Focused display

Dr. Spyros Giannelos, is a Research Scientist, leading energy projects using Mathematical Optimization & Data Science. Specifically such projects have been around energy investments with a focus on electricity. He holds a Doctor of Philosophy (Ph.D.) in Analytics & Mathematical Optimization from Imperial College London. His research interests include energy investments, optimization, data science, machine learning and quantitative finance.

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