A Why-Why Diagram is used to highlight the overarching problem and to deconstruct it into specific issues. In doing so, one can focus on areas for improvements and identify potential solutions. 

To develop a more accurate algorithm that would allow utility companies to predict short-term electricity load in Hoboken. Companies would benefit since they can optimize and improve demand response strategies, and consumers will face more reliable service, potential cost savings through better pricing strategies, and reduced risk of outages during peak demand periods.

Present State: There are challenges associated with inaccurate predictions and the inability of the model to adapt to real-time variables.

Desired State: A model that accurately incorporates real-time variables to predict the short-term electric load.

What is Known:

• Current challenges: Accuracy and integration of real-time variables

• Stakeholders: Utility companies and consumers

• Constraints: Data quality and regulatory constraints

• Objective: A more accurate model that predicts electric load with changing factors so that utility companies can benefit from improved demand strategies and better consumer experience.

Tasks to be Performed:

1. Data Collection: To be gathered from historical load data.

2. Data Preprocessing: The data will be cleaned by removing defective data and outliers. The data will also be addressed for missing values.

3. Feature Engineering: Ways in which the model could be improved. For instance, allowing the model to integrate real-time variables e.g. weather conditions and events.

4. Data Analysis: The trends would be identified and visualized to select the appropriate ML algorithm.

5. Model Selection

6. Training and Testing using the dataset

7. Implementation using Real-Time Predictions 

What?

• Electric load forecasting is necessary for utility companies to efficiently manage the generation, transmission, and distribution of electricity. Electricity demand is affected by factors such as weather, and time of day.

• The problem is that there lacks an algorithm that is able to accurately predict short-term electricity load while taking into account real-time variables.

When?

• This problem is more profound during peak demand periods, seasonal changes, weather changes, and during social events.

Who?

• Utility companies are affected because their goal is to optimize resource allocation and to improve demand response strategies.

• Consumers are also affected because they may experience times of unreliable service during peak demand periods, and higher costs.

Where?

• This problem is targeting the local energy grid in the Hoboken and the utility companies that provide service to that area.

Why?

• This is a problem because existing solutions may not be able to efficiently integrate real-time variables and respond to changing patterns. There also may be issues with the quality of the data used to train models.

How?

• The issue of inaccurate electric load forecasting models is related to other problems such as the poor management and allocation of energy resources, higher operation costs for the utility company, and consumer dissatisfaction because of unreliable service and increased bills.

• This problem would have developed because of several reasons.. For instance, traditional models may have been developed using historical data that did not capture changing patterns.