You will get Machine Learning Solutions in Power Systems
Project details
Our project focuses on developing advanced power consumption forecasting models to enhance energy management systems. Utilizing a comprehensive dataset, we implement both baseline and proposed models to achieve precise and reliable forecasts.
Baseline Models: We deploy ARIMA, SARIMA, LSTM, Prophet, LightGBM, and VAR models.
Proposed Models: We introduce Bidirectional LSTM (BiLSTM) and a hybrid CNN-BiLSTM model. BiLSTM captures dependencies in both directions of the time series, while CNN-BiLSTM leverages convolutional layers to extract spatial features.
Methodology: The project starts with data collection, cleaning, and transformation. Models are trained and validated using historical data, with performance evaluated on metrics such as MAE, RMSE, and MAPE. The best-performing model is selected for deployment, integrated with the client's system, and tested thoroughly.
Outcome: This project aims to provide an accurate, reliable, and efficient power consumption forecasting solution, enhancing the client's energy management capabilities and supporting better decision-making in energy usage and distribution.
Baseline Models: We deploy ARIMA, SARIMA, LSTM, Prophet, LightGBM, and VAR models.
Proposed Models: We introduce Bidirectional LSTM (BiLSTM) and a hybrid CNN-BiLSTM model. BiLSTM captures dependencies in both directions of the time series, while CNN-BiLSTM leverages convolutional layers to extract spatial features.
Methodology: The project starts with data collection, cleaning, and transformation. Models are trained and validated using historical data, with performance evaluated on metrics such as MAE, RMSE, and MAPE. The best-performing model is selected for deployment, integrated with the client's system, and tested thoroughly.
Outcome: This project aims to provide an accurate, reliable, and efficient power consumption forecasting solution, enhancing the client's energy management capabilities and supporting better decision-making in energy usage and distribution.
Machine Learning Tools
Azure Machine Learning, GitHub Copilot, Google AutoML, MLflow, NumPy, Python, Python Scikit-Learn, scikit-learn, TensorFlowWhat's included
| Service Tiers |
Starter
$130
|
Standard
$160
|
Advanced
$300
|
|---|---|---|---|
| Delivery Time | 7 days | 10 days | 14 days |
Number of Revisions | 1 | 3 | 5 |
Number of Model Variations | 1 | 2 | 4 |
Number of Scenarios | 1 | 3 | 5 |
Number of Graphs/Charts | 3 | 5 | 8 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | - | |
Source Code | - | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$60 - $90
Additional Model Variation
(+ 7 Days)
+$55
Additional Graph/Chart
(+ 2 Days)
+$20
Source Code
(+ 5 Days)
+$130Frequently asked questions
About Muhammad Umer
Machine Learning | Solar System Design | Industrial Automation
Rawalpindi, Pakistan - 2:39 pm local time
Steps for completing your project
After purchasing the project, send requirements so Muhammad Umer can start the project.
Delivery time starts when Muhammad Umer receives requirements from you.
Muhammad Umer works on your project following the steps below.
Revisions may occur after the delivery date.
Project Initiation
Kick-off Meeting: Discuss project scope, objectives, deliverables, timeline, and communication plan. Requirement Gathering: Understand client's requirements, data sources, and existing infrastructure.
Data Collection and Preprocessing
Data Collection: Gather historical power consumption data and relevant features like temperature, humidity, and time of day. Data Cleaning: Handle missing values, outliers, and ensure data consistency. Data Transformation: