CEO & Founder of Stat Modeller and SKILLXO
In today's world, optimizing energy consumption is crucial for sustainable development and cost-efficiency. With the advent of machine learning (ML) techniques, we now have powerful tools to analyze and predict energy usage patterns.
In this blog post, we will explore how ML can be leveraged to optimize energy consumption, leading to significant benefits for both the environment and businesses.
Computers are able to see, hear and learn. Welcome to the future.
- Dave Waters
One of our clients who is into manufacturing cosmetics products approached us to get solve their high energy consumption problem.
A game-changing solution has emerged to address this challenge: Machine Learning. By harnessing advanced algorithms and data analysis techniques, machine learning can uncover hidden patterns, identify influential variables, and accurately predict specific energy consumption.
The first crucial step in energy consumption optimization is understanding the key factors that significantly influence specific energy consumption.
The team had brainstormed and identified 45+ factors which are contributing to the energy consumption at the plant. Out of that, 14 were screened using EDA (Exploratory Data Analysis).
Now, it's time to gather the data for the shortlisted factors. The team had collected data for all 14 factors from the sensors' readings. Leveraging feature selection algorithms, we can identify the variables that play a significant role in specific energy consumption.
The factors were related to the different types of temperatures, RPM (Rotation per Minute), operation time and batch size. Sensors were capturing these reading every hour. Data of last-7 months was collected.
Before diving into the application of machine learning algorithms, it is crucial to ensure the data used for analysis is clean, accurate, and reliable. Data cleaning involves handling missing values, outliers, and inconsistencies that may exist in the dataset.
Techniques such as imputation, outlier detection, and data normalization can be applied to address these issues. By performing rigorous data cleaning, we can ensure that the machine learning models are built on a solid foundation, enhancing the accuracy and effectiveness of the energy consumption predictions.
Exploratory Data Analysis (EDA) is essential to gain insights into the dataset and understand its characteristics. EDA involves visualizing and summarizing the data through various statistical and graphical techniques. By examining the distribution of variables, identifying correlations, and detecting anomalies, EDA provides valuable information for feature selection and model building. Through EDA, patterns and trends within the data can be uncovered, helping to guide the subsequent steps of variable selection and model optimization.
The dataset was split into a training dataset, consisting of 70% of the data, and a test dataset, comprising the remaining 30%. The training dataset was utilized to develop and train various machine learning models, while the test dataset was used to evaluate their performance.
Here response or target variable was energy consumption level with 3 categories Low, Medium and High and hence classification algorithms were used.
Let's explore the algorithms employed in this energy optimization endeavor:
Decision Tree Classifier: Using a tree-like structure, the Decision Tree Classifier identifies patterns and makes decisions based on the data's attributes. It partitions the dataset into branches, enabling efficient classification of specific energy consumption.
Random Forest Classifier: The Random Forest Classifier combines multiple decision trees to form a robust ensemble model. By averaging the predictions of individual trees, it enhances accuracy and minimizes overfitting, making it a powerful tool for predicting specific energy consumption.
XGBoost Classifier: XGBoost (Extreme Gradient Boosting) is an optimized gradient boosting algorithm that excels in handling complex datasets. It leverages boosting techniques to create a strong predictive model by iteratively adding weak models to the ensemble.
K-Nearest Neighbours (KNN Algorithm): The KNN algorithm classifies data points based on their proximity to the k nearest neighbors. In the context of energy consumption prediction, it identifies similar instances to determine the specific energy consumption of a given observation.
Support Vector Machine: Support Vector Machine (SVM) is a versatile algorithm used for both classification and regression tasks. It constructs hyperplanes to separate data points into distinct classes, enabling accurate prediction of specific energy consumption based on identified patterns.
Logistic Regression: Logistic Regression is a popular algorithm for binary classification tasks. It models the relationship between the explanatory variables and specific energy consumption, providing valuable insights into the factors influencing energy consumption.
Multinomial NB: Multinomial Naive Bayes (NB) is a probabilistic classifier that extends the Naive Bayes algorithm to handle multiple classes. It assumes that each feature contributes independently to the specific energy consumption, making it efficient for large datasets.
AdaBoost Classification Model: AdaBoost is an ensemble learning technique that combines weak classifiers to form a strong predictive model. By iteratively adjusting the weights of misclassified observations, it focuses on challenging instances, enhancing the accuracy of specific energy consumption predictions.
ANN Classification Model: Artificial Neural Networks (ANNs) mimic the structure and functioning of the human brain. They consist of interconnected nodes (neurons) that process and learn from data. ANNs are effective in capturing complex relationships and have been successfully applied to energy consumption prediction.
After developing and evaluating the machine learning models on the test dataset, a comparison was made based on their accuracy scores.
Machine learning is transforming the approach to energy consumption optimization in industrial plants. By harnessing its capabilities, plant operators can uncover the influential variables driving specific energy consumption, select the most suitable machine learning model for prediction, and determine the optimal ranges for each explanatory variable. This data-driven approach empowers plants to achieve targeted specific energy consumption, leading to reduced costs, increased sustainability, and improved operational efficiency.
As industries continue to embrace machine learning, we can look forward to a future where energy optimization seamlessly integrates into plant operations, fueling a more sustainable and efficient world.
Machine learning is a journey of discovery, where algorithms unravel the secrets hidden within data, empowering us to make informed decisions and shape a better future. - Demis Hassabis
Originally published June 10, 2023
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