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Machine learning applications have been making waves across all industries, and the energy sector is no exception. From smart grid technology to predicting equipment failures to forecasting wind and solar power generation, applications of machine learning in energy sector are widespread.

Globally, the energy sector produces an incredible amount of data. Businesses are delving deeply into data using artificial intelligence (AI) and machine learning (ML) to make smarter decisions, achieve economic benefits, and generate predictions that prevent energy crises and expensive downtime. In fact, the market for AI in the energy sector is likely to expand by 29.88% between 2022 and 2029, reaching around USD 42.67 billion. This blog explores the use of machine learning in energy sector with some innovative and practical projects on machine learning in the energy industry.

Role of Machine Learning in Energy Sector

Machine learning and data analytics are valuable for governing and boosting the energy sector. Energy grids, renewable energy sources, and decentralized networks can benefit from the gradual integration of AI and ML to optimize energy usage. The future of the energy industry will be highly impacted by machine learning due to the widespread shortage of skilled personnel, growing dependency on smart technologies, and the demand for more economical and sustainable energy sources. Some of the useful applications of machine learning in energy sector include the following-

• Predictive Maintenance– machine learning analyzes historical and real-time data from various sources to determine which systems and subsystems are most likely to fail and when. Using machine learning, the equipment can be monitored in real-time, and any possible failures can be predicted in advance. This improves the overall efficiency of the equipment while saving high costs.
• Managing Power Grids- Keeping power generation and demand balanced at all times is one of the major concerns in managing power systems. Machine learning can improve durability and balance, particularly for renewable energy grids.
• Energy Demand Prediction- Energy demand forecasting is another potential use of machine learning algorithms in the energy industry. This is achieved by monitoring how each customer’s daily energy consumption varies over time. Machine learning in energy sector can be leveraged to optimize energy production for more efficient usage of resources and reduction in costs.

Here are a few renewable machine learning project ideas to help you better understand the applications of machine learning in energy sector-

1. Wind Energy Prediction using Long Short-Term Memory(LSTM)

This machine learning project aims to improve the forecasting accuracy of wind energy production to optimize the operation of wind farms using LSTM.

In the wind energy conversion techniques, like the dynamic management of wind turbines and power system scheduling, reliable short-term wind speed forecasts are highly practical and crucial. The wind speed, which has a predictable pattern over a set amount of time, is a key factor in the power generated created by the wind. In this project, you will leverage a time series pattern to gather relevant data for power prediction. The primary goal of this project is to increase the accuracy of forecasts for the power produced by wind energy. You will do so by using LSTM as a machine learning model and further optimizing it.

2. Global Warming Analysis and Prediction

Global warming analysis enables people to comprehend and address its consequences, motivates them to change their behavior, and supports their ability to respond to what is already a worldwide issue.  You can build a machine learning model to predict future changes in precipitation, temperature, and other meteorological metrics to provide insights that can inform policy-making and decision-making with regard to climate change.

In this project, you will explore the earth’s surface temperature change using various time series models (ARIMA, SARIMAX, Grid Search), cointegration, and causality analysis. You will create the models to forecast future temperatures and examine the effects of other factors on global warming, such as CO2 and population.

3. Solar-Energy-Prediction

In this project, you will use data from various weather variables to forecast the hourly power output of a photovoltaic power plant. Start this project by processing the raw meteorological data files from the National Oceanographic and Atmospheric Administration and the power production data files from the Urbana-Champaign solar farm. You will use models like boosting regression trees, weighted linear regression (with and without dimension reduction), and artificial neural networks(with and without vanishing temporal gradient).

4. Wind Turbine Classification

This is another useful project to highlight the use of machine learning in renewable energy. Making a wind farm as economically efficient as possible is crucial for making wind an affordable energy source.  This project aims to use classification algorithms on SCADA signals for a wind farm to simultaneously predict several wind turbine defects in advance. You will use three classification algorithms- decision trees, random forests, and k nearest neighbors, and test them using imbalanced and balanced training data.

5. Smart Grid Stability Prediction

The main objective of this machine learning project is to use machine learning algorithms to predict and prevent power grid instability for an efficient and reliable power grid with fewer outages.

In a smart grid, customer demand data is gathered, centralized supply and demand analysis is done, and the proposed price data is delivered to users to decide on usage. This deep learning project aims to apply Keras’ Sequential model to achieve the most accurate predictions possible. In this machine learning project, you will use a dataset including outcomes from grid stability simulations for a sample 4-node star network. Use a sequential artificial neural network (ANN) with a single-node output layer, three hidden layers with 24, 24, and 12 nodes, and a single input layer with 12 input nodes. Additionally, you will assess the impact of the deep learning architecture (the volume and size of hidden layers), the number of epochs, and the significance of dataset augmentation.

Source: projectpro