Data Input

• Historical load data is input into the model.

Data Processing Module

• The data is cleaned and normalized.

Exploratory Data Analysis Module 

• Any trends are identified and data visualization techniques are implemented.

Feature Engineering Module

• New features are created based on existing data.

Model Selection Module

• An appropriate machine learning algorithm is selected.

Model Training Module

• The selected model is trained.

Prediction Output

• Predictions are generated.

Improvements to be made?

• The predictions and actual load data are compared. If there is the need for adjustments or improvements, the model is retrained and re-evaluated.

Data Input

• The user should input real-time electricity load data and external factors (e.g. weather, special events, holidays) into the system.

Data Preprocessing

• The algorithm cleans the input data by removing any erroneous or missing values, and normalizes it for consistency.

Feature Engineering

• The system automatically creates new features relevant to the data input.

Run the Machine Learning Model

• The algorithm runs the machine learning model using the prepared data.

Prediction Output

• Short-term electricity load forecasts are generated.

Decision Making

• The user should analyze the predictions and make decisions that would influence operational efficiency, resource allocation or demand management strategies.

Monitoring and Feedback

• The user should monitor and evaluate the performance of the model by  comparing the predictions with actual electricity loads recorded for that period. Based on the feedback, adjustments should be made to the algorithm or the model should be retrained with new data.

Reporting

• The user should then be able to generate reports to summarize predictions and insights for stakeholders.