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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Introduction to Energy Analytics 101

N
Nitin Last Updated : 19 Jun, 2023
6 min read

This article was published as a part of the Data Science Blogathon.

Introduction

AI and machine learning are two of our time’s buzz-worthy business terms. As a result, businesses across all industries are looking to implement these technologies to improve and automate their core processes. And the energy industry is no exception.

Renewable energy companies or clean energy corporations (comprising solar, wind, hydro, battery, and nuclear) have harvested the power of machine learning over the years. It has been possible for them to reduce their costs, improve their predictions, and increase the rate of return on their portfolio. The trend is likely to continue at an even more significant speed.

If your company is involved in the energy industry – or consumes a large amount of electricity – there are chances that machine learning and artificial intelligence can boost its performance. But how exactly? Let’s take a closer look.

How has Machine Learning Overhauled the Energy Sector?

 

energy analytics
Courtesy: Image via Enappsys

There are so many ways in which machine learning/AI can be utilized to transform the energy sector positively. Listed below are a few of the most popular applications currently in development.

A. Grid Management System

In the energy field, grid management is one of the most exciting applications of artificial intelligence. Electricity is generated & delivered to customers through a set of a complex networks (also referred to as the power grid). One of the challenges with the power grid is that power generation and demand must always match. Otherwise, blackouts and failures can occur.

Although there are many means of storing energy, the most common method is pumped hydroelectric storage, which involves pumping water to a specific elevation and then harnessing it again by allowing it to fall onto turbines.

It is difficult to predict how much electricity the grid will produce using renewable energy. After all, it is contingent upon several factors, such as sunlight and wind.

B. Demand Forecast

Large swings in demand can make it very expensive for countries that produce most of their energy using renewable energy sources. With most countries shifting towards green energy, it is becoming increasingly difficult to react effectively to swings in demand. For example, Germany plans to use renewable energy to cover 80% of its electricity consumption by 2050.

In countries such as Germany, there are two significant challenges to overcome. The first is swings in demand. It is not uncommon for electricity demand to skyrocket on a specific day or period of the year (for example, on Christmas). The second is whether volatility. In either case, supplemental stations or fossil fuel-powered facilities may be required to satisfy the excess demand if the wind does not blow or the sky is cloudy.

C. Predictive Maintenance (Fault Detection)

Apart from matching energy production with energy consumption, artificial intelligence is also a significant factor in ensuring power grid reliability and robustness.

In 2003, there was an event when a massive blackout in Ohio was triggered by a low-hanging high voltage power line brushed against an overgrown tree. There was no indication the incident had occurred, as the power system alarm failed. The electric company discovered the issue after three more power lines fell for similar reasons. Ultimately, this oversight failed the entire grid.

In addition, the blackout lasted for two days and affected 50 million people, 11 people died, and about $6 billion was lost.

D. Demand Side Response Interruptions

Many countries partner with companies to analyze and predict weather data and electricity. To forecast the amount of wind and solar energy to be generated at any given time, Germany initiated a project with EWeLiNE. As a result, the country can cover excess electricity demand by using non-renewable energy whenever required.

They incorporate enormous historical data sets to train their machine learning algorithms to effectively predict weather and power changes. As well as data collected from wind turbines or solar panels, they also utilize historical data sets to train their machine learning algorithms.

E. Trade & Schedule Automation

Critical applications are sourced using AI and machine learning to ascertain the best way to use your energy flexibility on your site and within the energy market in real-time. These include:

  • Price Optimization
  • Balancing Services & Demand Side Response
  • Energy Market Trading

Some common dataset used for forecasts and automating energy controls based on several sources includes:

  • Aggregated site data
  • Individual site data
  • Grid frequency data
  • Historical grid data
  • Pricing in traded energy markets
  • Weather forecasts
  • Historical weather data
energy analytics

 

Courtesy: Image via Enappsys

Evolution of the Internet of Things (IoT)

Using machine learning techniques, predictive maintenance can be implemented. Sensors collect operation time-series data from Power lines, machinery, and stations (data with a timestamp).

Machine learning algorithms can detect whether a component is about to fail in X amount of time (or n-steps). Furthermore, they can also expect the remaining useful life of machinery or when the subsequent failure will occur. In addition to predicting machine failure efficiently and preventing outs and downtimes, these algorithms optimize maintenance activities and periodicity, thus reducing maintenance costs.

For example, the United Kingdom started installing phasor measurement units to prevent power line failures. They can measure:

  • A voltage meter and a current meter
  • GPS (global positioning system)
  • Timing (in microseconds)
  • Device Identification

As a result, events such as the London blackout can now be prevented entirely. Ultimately, AI and machine learning can help energy companies shift from reactive to predictive maintenance.

Identify New Resources

In addition to weather forecasting and renewable energy source optimization, artificial intelligence is used to explore and drill for fossil fuel energy sources.

The robotic device will be used to explore, gather data above the floor, and conduct an analysis based on the information to identify new locations for drilling oil and natural gas.

Smart Energy Consumption

A switch to renewable energy sources is not just a matter for governments and electric companies. Companies like Google and Microsoft are reducing their energy consumption to impact the environment and their bottom lines positively.

Google is known for its intelligent data centers established all over the world. These data centers generate an enormous amount of heat, which needs a great deal of electricity to cool.

DeepMind AI implemented state-of-art machine learning models to churn down on energy cooling on its Google data centers by 40%; this reduced their utility costs and helped reduce overall emissions, reducing their carbon tax obligations.

energy analytics

Courtesy: Image via Visual Capitalist

With developed countries striving for a completely green economy, maintaining a secure, balanced, and reliable power grid is a priority. Machine learning and artificial intelligence certainly have a long way to go in the energy sector.

That is implementing 100% renewable energy sources, AI-controlled power distribution, and grid smart management to create a digital power grid that enables two-way communication between utility companies and consumers.

It is the goal of smart grids to provide consumers with continuous data collection and display so they can further improve their energy consumption behaviors through smart meters, sensors, and alerting devices. As well as predicting demand, improving performance, reducing costs, and preventing system failures, it can also be fed into machine learning algorithms.

While smart grids are being implemented in several developed countries, there is still a long way to go before implementing 100% renewable energy sources, AI-controlled power distribution, and grid management.

Frequently Asked Questions

Q1. What is energy analytics?

A. Energy analytics refers to the process of collecting, analyzing, and interpreting data related to energy consumption, production, and efficiency. It involves using advanced technologies and techniques to extract insights and optimize energy usage, identify patterns, and make informed decisions for improving energy performance, reducing costs, and minimizing environmental impact.

Q2. What is the scope of energy analyst?

A. The scope of an energy analyst involves analyzing energy usage, identifying inefficiencies, and proposing strategies for optimizing energy consumption. They assess energy data, conduct energy audits, evaluate energy-saving technologies, and provide recommendations for reducing costs and environmental impact. Energy analysts may work in various sectors, including buildings, manufacturing, transportation, and utilities, aiming to enhance energy efficiency, sustainability, and overall energy performance.

Conclusion

In energy, machine learning is booming, with new applications emerging every year. Everything except the energy generation is set to be improved by data and technology.

The key is balance. AI can be good in theory, but it cannot be at the expense of the continued emission of greenhouse gases. E.g., computer vision and geospatial analysis are still being used to identify natural gas reserves, pushing us away from our commitment to net-zero and fueling our continued reliance on fossil fuel.

AI is undoubtedly the future of the clean/green energy revolution. It is optimistic about its implementation in applications such as green residential and commercial construction development, grid management, and predicting the uncertainty in non-synchronous fuel. Well, that’s exciting!!

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

N
Nitin

I'm a Data Science professional with 6 years of experience and a solid foundation in Computer Science Engineering & Statistics. I graduated with a master's degree in Data Science and Analytics from University College Cork and a professional diploma in Project Management from UCD Professional Academy.

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