Everything you need to know about this community

Start or transition into a secure career in the energy sector, regardless of your location, age, sex, education level, or work experience.

The energy sector is resilient and strong, with potential for growth and good compensation, with the ability to work remotely or in -person, whether you like coding or not. I am bringing you years of MSc + PhD + Postdoctoral Research + Consultancy (Industry) experience at a very low cost (a similar program would cost 100 times more with no exaggeration). No academic terminology. Real-world beginner-friendly practical skills to secure a career. Advanced courses for PhD preparation also available! This is the 'Energy Data Scientist' program.

At the top of the screen, the 'Map' shows the approximate location of all members of this community.

At the top of the screen, the 'Classroom' includes all the resources you need.

Everything in Classroom comes with support from Dr Giannelos. This includes daily Q&A, and a personalized curriculum.

Items 1.1 - 1.35 are online courses teaching data science for energy.

Items 2.1-2.6 are online courses teaching machine learning for energy.

Items 3.1-3.5 are online courses teaching software engineering for energy.

Items 4.1-4.44 are online courses teaching optimisation for energy.

Items 5.1-5.20 are online courses teaching energy-economics/finance.

Items 6.1a and 6.1b include reports on energy research, written in simple, beginner-friendly language. Access to energy-research is only possible by subscribing to databases like ieee-explore (would cost you hundreds of dollars per month). Dr Giannelos brings you the key points along with his commentary and extra details, all written in simple beginner-friendly language. This will offer you unprecedented insights that will help you stand out.

Item 6.3 includes career guides, job opportunities, and other career-related material.

Item 6.5 includes reports on energy-economics books, written in simple, beginner-friendly language (such access would cost you a lot!)

Item 6.2 includes reports on the energy industry (economics, finance, investments etc) written in simple, beginner-friendly language. Dr Giannelos reads energy-economics/finance articles from Financial Times, Wall Street Journal, the Economist, Investors' Chronicle, and brings you their key points along with his commentary and extra details you need to know, all written in simple beginner-friendly language! No need to subscribe to any of these (Financial Times etc) because your Skool subscription offers the info you need at a fraction of the cost.

Item 7.1. gives you a certificate and access to a real-world project.

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START WITH THE FOLLOWING 65 BEGINNER-FRIENDLY COURSES

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1) Hourly Electricity Demand at Busbar Level. Go to Classroom --> 1.27

2) Hourly Electricity Demand for All Grid Buses. Go to Classroom --> 1.28

3) Drivers for Electricity-Grid Reinforcement: Go to Classroom --> 5.2

4) Per Unit System for Power-System Analysis. Go to Classroom --> 4.1

5) Comparing the Electricity-Generation Mix. Go to Classroom --> 1.22

6) Calculating the Installed Generation Capacity. Go to Classroom--> 1.23

7) Electricity Demand: Power versus Energy. Go to Classroom --> 1.2

8) Comparing Countries’ Generation Capacity. Go to Classroom --> 1.18

9) Comparing Countries’ Electricity Consumption. Go to Classroom --> 1.24

10) The Global Installed Capacity of Renewables. Go to Classroom --> 1.34

11) Levelised-Cost-of-Energy (LCOE) - in Python. Go to Classroom --> 1.12

12) Drafting Electricity Grid Schematics. Go to Classroom --> 1.4

13) Net Electricity Demand & Wind Share. Go to Classroom --> 1.11

14) Total and Residual Load-Duration Curves. Go to Classroom --> 1.25

15) Baseload-and Intermediate-Load Levels. Go to Classroom --> 1.26

16) Power-Station Data Analytics. Go to Classroom --> 1.6

17) Connecting Offshore Wind Farms to Onshore. Go to Classroom --> 1.3

18) Rolling Averages of Electricity Demand. Go to Classroom --> 1.29

19) Comparing Electricity Access across Countries. Go to Classroom --> 1.19

20) Comparing CO₂-Emissions Intensity. Go to Classroom --> 1.20

21) Comparing Countries’ Population in Python: Go to Classroom --> 1.21

22) Aggregating & Summarising Electricity Data. Go to Classroom --> 1.30

23) Financial Discounting in Energy. Go to Classroom --> 5.3

24) Capitalisation Factor for Energy Investments. Go to Classroom --> 5.7

25) Using Renewables Ninja in Python. Go to Classroom --> 1.31

26) Data Science on Installed-Capacity Datasets --> 1.35

27) Energy Storage Data Analytics. Go to Classroom --> 1.5

28) Accessing & Processing ENTSOE Data in Python. Go to Classroom --> 1.32

29) Preparing Excel Data for Pivot-Table Analysis. Go to Classroom --> 1.9

30) Big-Excel Processing Techniques in Python. Go to Classroom --> 1.33

31) Immigration across Countries & Years. Go to Classroom --> 4.3

32) Introduction to Visual Studio Code for Python. Go to Classroom --> 3.2

33) Essential Python for Energy Analysis. Part 1 and 2. Go to Classroom --> 1.1

34) Reducing the dimension of Electricity Data. Go to Classroom --> 1.13

35) Day -Ahead & Balancing Market in Python. Go to Classroom --> 5.17

36) Retail Electricity Market in Python. Go to Classroom --> 5.18

37) Oil Forward Contracts in Python. Go to Classroom --> 5.19

38) Oil Option Contracts in Python. Go to Classroom --> 5.20

39) Wholesale Electricity Price in Python. Go to Classroom --> 5.6

40) Univariate Deep Learning for CO₂Forecast. Go to Classroom --> 2.4

41) Object-Oriented Programming for Energy. Go to Classroom --> ID: 3.1

42) Software Design for Energy Modelling. Go to Classroom --> 3.4.

43) Intro to Optimisation in Pyomo, GAMS & Mosel. Go to Classroom --> 4.4

44) Essential Optimization Concepts with Mosel. Go to Classroom --> 4.6

45) Building Optimization Models in Mosel. Go to Classroom --> 4.5

46) Capacity-Expansion Planning with Storage. Go to Classroom --> 4.29

47) Grid-Reinforcement Optimisation for India. Go to Classroom --> 4.10

48) Optimal Siting of Onshore Wind Farms. Go to Classroom --> 4.8

49) Optimal Siting of Hydroelectric Plants. Go to Classroom --> 4.9

50) Locational Marginal Electricity Price Model. Go to Classroom --> 4.27

51) Spot-Market Strategy for Power Stations. Go to Classroom --> 4.31

52) Energy Storage Allocation at minimum cost. Go to Classroom --> 4.12

53) Economic Dispatch for Grid with Renewables. Go to Classroom --> 4.14

54) Εconomic Dispatch for Thermal-Only Grids. Go to Classroom --> 4.13

55) Economic Dispatch with Wind & Storage. Go to Classroom --> 4.16

56) Optimisation: Natural Gas & Electricity Grids. Go to Classroom --> 4.24

57) Economic Dispatch with Hydro Generation. Go to Classroom --> 4.19

58) Profit Maximisation in Energy+Reserve Markets. Go to Classroom --> 4.18

59) DC-Power-Flow Modelling. Go to Classroom --> 4.7.

60) Economic Optimisation for Wind Manufacturers. Go to Classroom --> 4.25

61) Optimisation, Reinforcement Learning & Risk. Go to Classroom --> 4.40

62) Economics of Supply Chain Networks for Coal. Go to Classroom --> 4.42

63) Technical Characteristics of Power Stations. Go to Classroom --> 5.1

64) Energy Subsidy Mechanisms: CfD, ROC & FIT. Go to Classroom --> 5.8

65) Energy-Balance Data Analysis. Go to Classroom --> 5.12.

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ARE YOU ADVANCED? CONTINUE WITH THE FOLLOWING 45 COURSES

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66) Geopolitical Mapping of Military Alliances. Go to Classroom --> 1.7.

67) Mapping Energy Infrastructure with Python. Go to Classroom --> 1.10.

68) Big Data for Power Grid Reinforcements. Go to Classroom --> 1.17.

69) Calculating Zone-Level Electricity Demand. Go to Classroom --> 1.15

70) Modelling Flat and Peaky Load Profiles. Go to Classroom --> 1.16.

71) Univariate Linear Regression for CO₂-Forecasts. Go to Classroom --> 2.1.

72) Univariate ARIMA for CO₂-Forecasts. Go to Classroom --> 2.2.

73) Univariate Shallow Neural Nets for CO₂Forecast. Go to Classroom --> 2.3.

74) Comparing Machine-Learning Forecasts. Go to Classroom --> 2.5.

75) Multivariate Machine Learning for Forecasts. Go to Classroom --> 2.6.

76) Readable Python with Logging & Type Annotation. Go to Classroom --> 3.3.

77) Multithreading Essentials for Energy Modelling. Go to Classroom --> 3.5.

78) Backwards-Induction for Grid Reinforcement. Go to Classroom --> 4.2.

79) AC-Power-Flow Modelling. Go to Classroom --> 4.11.

80) Economic Dispatch for Heat-Electricity Grids. Go to Classroom --> 4.15.

81) Environmental Dispatch for Electricity Grids. Go to Classroom --> 4.17.

82) Economic Dispatch - Electricity-Hydro Systems. Go to Classroom --> 4.20.

83) Pareto Economic Analysis for Heat-Electricity. Go to Classroom --> 4.21.

84) Load & Generation Shifts on Power Flows. Go to Classroom --> 4.22.

85) Unit Commitment Optimisation. Go to Classroom --> 4.23.

86) Optimising Industrial Energy Systems. Go to Classroom --> 4.26.

87) F-Factor Analysis for Energy Storage. Go to Classroom --> 4.28.

88) Pareto Analysis for Electricity Systems. Go to Classroom --> 4.30.

89) Spinning Reserve for Electricity Grids. Go to Classroom --> 4.32.

90) Switchable Transmission-Line Modelling. Go to Classroom --> 4.33.

91) Transmission-Expansion Planning Optimisation. Go to Classroom --> 4.34.

92) Demand-Response for Cost-Optimal Operation. Go to Classroom --> 4.35.

93) Smart-Power-Flow Technologies & Line Upgrades. Go to Classroom --> 4.36.

94) F-Factor Analysis for Vehicle-to-Grid Systems. Go to Classroom --> 4.37.

95) Travelling Salesman Problem for Energy. Go to Classroom --> 4.38.

96) Linearising Non-Linear Objective Functions. Go to Classroom --> 4.39.

97) Endogenous & Exogenous Uncertainty in Power. Go to Classroom --> 4.41

98) Stochastic Optimisation & Backwards Induction. Go to Classroom --> 4.43.

99) Grid Reinforcement with DLR & Storage. Go to Classroom --> 4.44.

100) Incremental & Strategic Grid Reinforcement. Go to Classroom --> 5.4

101) Smart Grid for Network Reinforcement. Go to Classroom --> 5.5.

102) Investment Cost Modelling - Spackman Method. Go to Classroom --> 5.9.

103) Valuation of Demand-Side Response Contracts. Go to Classroom --> 5.10.

104) Technology S-Curve Analysis with Python. Go to Classroom --> 5.11.

105) Environmental Economics - Marginal Abatement. Go to Classroom --> 5.13.

106) Investment Costs and Smart Grid Deployment. Go to Classroom --> 5.14.

107) Smart-Charger Investments under Uncertainty. Go to Classroom --> 5.15.

108) Option Value Analysis of Smart Technologies. Go to Classroom --> 5.16.

109) Upsampling Daily to Hourly Electricity Data. Go to Classroom --> 1.14.

110) Comparing Datasets with Python & Excel. Go to Classroom --> 1.8.

111) Smart Meter Big Data Efficient Processing --> 1.36

112) Download Electricity Market Data Via API --> 1.37

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🏛️ Dr Spyros Giannelos ( spyros.giannelos@gmail.com )

PhD in Energy, Imperial College London
40+ research publications, over 1100 citations. View profile
Author on Substack. View profile
Energy Data Science consultant at www.energydatascientist.com
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Energy Data Scientist

skool.com/software-school-for-energy-7177

A beginner-friendly training program for a career in the energy sector. Regardless of your location, age, sex, education or experience level.

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