Data Analysis On Datasets For Energy (Economics Studies)

Data Analysis on Big Energy Data with focus on Economics/Investments

What you'll learn
Learn how to perform Data Engineering across ALL possible Energy Datasets; more details shown in the promo video and in the contents below.
Learn the skills that will be required for the Green Energy Revolution, which is all about Data Science.
Register onto my website for extras and for networking i.e. to stay in contact and for possible future collaboration.
Learn how to Implement Big Data methodologies for Energy Datasets.
Learn how to apply Data Analysis for Energy Investments.
Apply Data Analysis on Energy Storage, Electricity Generation and Demand.
The subtitles are manually created so they are fully accurate. They are not auto-generated.
Requirements
We go through every Python command, in detail, Step by Step , so you can start even with zero knowledge / experience in programming.
Description
Summary: This course uses data science on datasets that are used in energy / economics studies. Assume that you are a consultant and a company hires you to conduct data analysis on an energy project. This course gives you all the skills you need because it is based on real world industry projects.All using Data Science with the programming language Python. Step- by - step, no prerequisites.All videos have accurate subtitles (i.e. not the auto-generated ones, but accurate ones for maximum clarity). About:Research Fellow, leading the Research in industry projects in Mathematical Optimization & Data Science applied to Energy Investments. Having a PhD in Analytics and Mathematical Optimization, applied to Energy Investments, from Imperial College London.And an M. Eng. degree in Power System Analysis (Electricity) and Economics. The old energy landscape is steadily being replaced by a new energy landscape that produces and consumes Big Data. To understand the new energy landscape we therefore need to adapt and make use of state-of-the-art Big Data algorithms and methods based on the latest advances on Data Science & Optimization.Important:No pre-requisites are needed:  You do not have to know Programming (eg Python or MATLAB or C ++) at ALL because we go through all the commands needed , in great detail and with many examples. 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. If you are an experienced programmer, then you may find that the videos go very slowly. This is true because  every command, especially the complex ones, is broken down into simpler ones. There are other online courses on Udemy that simply give the code, and give you an overall description of it and then you have to figure out what it does and if there is something you don't understand then you may never get a response. But, in this course we do the opposite: we go very slowly and examine every command. This is why some videos may be 30 minutes long - it is because we go slowly and making sure everything is explained.  In this course, there is NOTHING for you to search around on google because every line of code is explained in detail. So in the end of the course you will feel confident that you OWN everything that has been taught!   For the contents of this course, please watch the promo video. This course has received top-class reviews, on a consistent basis. This course has also been designed based on interview material (banks, energy companies/ organisations, software engineering roles etc) so by the end of it, you will feel confidence in your knowledge.

Overview

Section 1: Big Dataset Comparisons

Lecture 1 Anaconda and Python

Lecture 2 Compare Large Datasets (excel)

Lecture 3 Compare Code (text files) through a special software (we download it for free)

Lecture 4 Compare two Folders through a special software (we download it for free)

Lecture 5 Compare two datasets through the MATCH excel function - step by step

Lecture 6 Compare large Datasets Using Python (Application to Energy Datasets)

Section 2: Protection of Sensitive Datasets (concealing)

Lecture 7 360 Overview

Lecture 8 New Research

Lecture 9 Use Python to conceal sensitive information by making a dummy dataset

Section 3: Data Analysis on Electricity Demand data

Lecture 10 360 Overview

Lecture 11 New Research

Lecture 12 Normalization & Load Factor Profile using EXCEL

Lecture 13 Python code: Normalization of a load profile

Lecture 14 Develop a load profile with set peakiness level using EXCEL

Lecture 15 Develop a load profile with set peakiness level on Python

Lecture 16 Rolling Average on Python. Showing it with a 3-hour interval

Lecture 17 Data for Electricity Demand raw format

Lecture 18 Total system demand, using Python groupby

Lecture 19 Python Pivot Table for Calculating the Total demand per bus

Lecture 20 Section analysis of an electricity grid using Python

Lecture 21 Annual demand per bus, at different levels of time granularity, using Python

Section 4: Data Analysis on Time-Granularity

Lecture 22 360 Overview

Lecture 23 New Research

Lecture 24 From 48 to 24 hourly time periods using MATLAB

Lecture 25 From 48 to 24 hourly time periods using Python

Lecture 26 Selecting data with a 3-hour frequency using Python

Lecture 27 Selecting fewer observations out of 8760 using Python

Lecture 28 Using Python to convert a 365-long dataset to an 8760-long one.

Section 5: Data Analysis on Energy Storage using Python

Lecture 29 360 Overview

Lecture 30 Energy Storage Capacity on Excel & Python

Lecture 31 Total Storage capacity & average efficiency on Excel & Python

Section 6: Data Analysis on Electricity Generation using Python

Lecture 32 360 Overview

Lecture 33 Processing & cleaning the data using Python

Lecture 34 Extending the time-series to cover a year using Python

Lecture 35 Categorizing the data per generator type, year, bus etc using Python

Lecture 36 Calculating wind patterns & placing them in the dataframe, using Python

Lecture 37 Per type, Per busbar and Total system generation capacity using Python

Lecture 38 Effect of Transmission & Distribution losses on Generation Capacity using Python

Section 7: Big Data Methodology on an Electricity dataset

Lecture 39 Summary

Lecture 40 Description of the Methodology & Associated risks

Lecture 41 Structure of Big Data on Electricity Demand

Lecture 42 Step1: Calculation of seasonal statistics for the load factor using EXCEL

Lecture 43 Step 1 using Python

Lecture 44 Step2 - Calculation of daily average Load Factor time-series

Lecture 45 Step 2 using Python

Lecture 46 Step3 - Finding the error of daily average from seasonal load factors

Lecture 47 Step 3 using Python

Lecture 48 Step4 - Calculation of the minimum Load Factor Error

Lecture 49 Step 4 using Python

Lecture 50 Step5 - Finding the day where the minimum error occurs

Lecture 51 Step5 using Python

Lecture 52 Step6: Calculation of the small dataset that has the typical days

Lecture 53 Step6 using Python

Section 8: Producing Pivot and Summary tables and electricity - related graphs on Python

Lecture 54 Python: Summary table for an electricity system

Lecture 55 Python: Producing a Pivot-Ready excel file

Lecture 56 Python: From Big-Data to Pivot-Tables on Excel

Lecture 57 Python/Excel: Differences in the use of Scatterplots & Line graphs

Section 9: Analysis on Energy Investments using Python

Lecture 58 Identifying mutually exclusive investments

Lecture 59 Grouping the mutually exclusive investment strategies

Lecture 60 Filtering investment strategies

Lecture 61 Comparison of Investment solutions

Section 10: Dataframe Operations for Large Datasets

Lecture 62 Dataframe Operations for Big Energy Datasets

Lecture 63 Python: Updating an existing dataset for electricity demand

Lecture 64 Replace existing generation types with new ones in the Generation dataset

Section 11: Analysis on Electricity Demand curves on Python

Lecture 65 Python: Residual Load Duration Curve for investment assessment

Lecture 66 Python: Load Duration & Residual Load Duration curves for investment assessments

Lecture 67 Decomposition of the electricity load duration curve

Section 12: BONUS

Lecture 68 Extra knowledge

Entrepreneurs,Economists.,PhD / MSc students.,Data Scientists / Analysts / Engineers working in the energy field.,Software Engineers.,Anyone interested to gain the Absolutely Necessary skills for getting a job in the field of Energy (energy transition, sustainability, climate change etc).,Finance professionals working with Energy Datasets.