Data Privacy and Security
Hazard: The dataset being used may contain sensitive data about electricity consumption, and there may be privacy concerns from households and businesses. There can be data breaches or unauthorized access.
Solution: There shall be compliance with data privacy laws, and encryption techniques shall be adopted when processing the data.
Biases in the Data
Hazard: The dataset might have real-time variables that are underrepresented such as certain weather conditions or special events. This may affect the accuracy of the model's predictions.
Solution: There shall be a diverse representation of these variables and the model shall be tested across various scenarios to improve accuracy.
Misuse of Predictions
Hazard: Stakeholders, such as energy companies and government regulators, may make decisions that may impact vulnerable communities. For instance, inaccurate predictions could lead to unfair pricing or energy shortages. This would unfairly prioritize certain consumers over others.
Solution: Ethical guidelines shall be established for the use of the predictions.Â