Quantifying Energy Investments Using Data Science

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
Requirements
The only prerequisite is to take the first course of the "giannelos dot com" program , which is the course "Data Science Code that appears all the time at workplace".
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: 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 investments  (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.  Just follow what is shown on screen, because we go slowly and explain everything in detail.

Overview

Section 1: Introduction

Lecture 1 Overview

Lecture 2 Analysis

Section 2: The Theory of Energy Investments

Lecture 3 Decarbonization-driven Energy Investments

Lecture 4 Flexibility and Energy Investments

Lecture 5 Future Peak Load as a Signal for Investments

Lecture 6 The concept of build time of electricity infrastructure assets

Lecture 7 The concept of the economic lifetime of an electricity infrastructure asset

Lecture 8 The concept of stranding risk of electricity infrastructure investments

Lecture 9 Python: Investments are in GW, and not in GWh. Demonstration of the differences.

Section 3: Investment combinations

Lecture 10 Investment combinations for offshore farms

Section 4: Analysis on Energy Investments using Python

Lecture 11 Identifying mutually exclusive investments

Lecture 12 Grouping the mutually exclusive investment strategies

Lecture 13 Filtering investment strategies

Lecture 14 Comparison of Investment solutions

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

Lecture 15 Introduction

Lecture 16 Producing the S-curve using different probability distributions

Lecture 17 Exponential & Sigmoid Learning curve, Experience curve for novel investments

Section 6: Annualization of expenses

Lecture 18 Annualizing costs using excel

Lecture 19 Annualizing costs using python

Lecture 20 Capitalization factor with changing discount rates

Lecture 21 Three ways to calculate the capitalization factor

Section 7: Costs

Lecture 22 Fixed and variable cost calculation

Lecture 23 Per unit costs

Lecture 24 The famous Spackman method

Section 8: Discount Factors

Lecture 25 The concept of epoch and horizon

Lecture 26 Discount factor - reading from Excel

Lecture 27 Discount factor - constructing using Python

Lecture 28 Cumulative discount factor for operational costs

Lecture 29 Cumulative discount factor for investment costs

Section 9: Bonus

Lecture 30 Extra

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