Electricity Demand Analysis Using Data Science
Last updated 10/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.55 GB | Duration: 3h 14m

Using Python
What you'll learn
How to actually use Data Science to gain insights about Energy Storage
Modelling key concepts of electricity demand: load factors, normalization, peakiness, plots
Specialized electricity demand analyses - sector analyses
Duration curves - residual, load duration, decomposition
Data analysis on electricity demand - pivot tables, updates
Country-level electricity demand analyses
Part of the giannelos dot com official certificate for high-tech projects.
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 аbout:This course teaches how to use Data Science in order to get insights about Electricity Demand. First, we explore fundamental concepts about electricity demand such as the load factors, normalization, peakiness as well as how to accurately plot the electricity demand.We then mention a special case of demand analysis done with electricity grids.Furthermore, we model electricity demand duration curves: net load, residual load duration curve, and decomposition.We also conduct data analysis on electricity demand datasets as well as calculate the total annual energy demand of a country.Who:I am a research fellow and I lead industry projects related to energy investments using mathematical optimisation and data science. Specialized in the Data Science aspect of the Green Energy transition, focused on algorithmic design and optimisation methods, using economic principles. Doctor of Philosophy (PhD) in Analytics & Mathematical Optimization applied to Energy Investments, from Imperial College London , and Master of Engineering (M. Eng.) degree in Power System Analysis (Electricity) and Economics .Special Acknowledgements:To Himalaya Bir Shrestha, senior energy system analyst, who has been contributing to the development of Python scripts for this course and who regularly posts on medium. Important:No pre-requisites and no experience required.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 understand everything in detail.
Overview
Section 1: Introduction
Lecture 1 Overview
Section 2: Key concepts of electricity demand
Lecture 2 Normalization & Load Factor Profile using EXCEL
Lecture 3 Python code: Normalization of a load profile
Lecture 4 Develop a load profile with set peakiness level using EXCEL
Lecture 5 Develop a load profile with set peakiness level on Python
Lecture 6 Python/Excel: Differences in the use of Scatterplots & Line graphs
Section 3: Specialized Electricity Demand analyses
Lecture 7 Finding the demand at different regions of the electricity grid
Section 4: Duration curves
Lecture 8 What is Net Load ? (Python demonstration)
Lecture 9 Residual load duration curve using Python
Lecture 10 Load duration curve vs Residual Load duration curve using Python
Lecture 11 Decomposition of load duration curve: demand supplied by thermal vs renewables
Section 5: Data Analysis on Electricity Demand
Lecture 12 Data for Electricity Demand raw format
Lecture 13 Python Pivot Table for Calculating the Total demand per bus
Lecture 14 Total system demand, using Python groupby
Lecture 15 Annual demand per bus, at different levels of time granularity, using Python
Lecture 16 Updating one of the components of demand
Section 6: How to find the total annual energy demand in a country
Lecture 17 Finding the total demand of a country
Lecture 18 Comparing countries on their electricity consumption over different years
Section 7: Bonus
Lecture 19 Extras
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
Screenshots