Load forecasting using AI – MCQs – EE

1. What is the main goal of load forecasting in power systems?
(A) To predict future electricity demand
(B) To measure voltage drops
(C) To calculate transformer ratings
(D) To design new transmission lines

2. Which AI technique is most commonly used for load forecasting?
(A) Artificial Neural Networks (ANN)
(B) Boolean Logic
(C) PID Controllers
(D) Ohm’s Law

3. Load forecasting helps power utilities to:
(A) Plan generation and optimize resources efficiently
(B) Increase power losses
(C) Reduce reliability
(D) Delay maintenance

4. Short-term load forecasting is typically used for:
(A) Daily or hourly demand prediction
(B) Long-term planning
(C) Infrastructure investment
(D) Yearly demand only

5. Long-term load forecasting assists in:
(A) Planning generation capacity and infrastructure development
(B) Real-time fault detection
(C) Switching circuits
(D) Overcurrent protection

6. Which input parameters are most commonly used for load forecasting?
(A) Historical load, temperature, and time data
(B) Only voltage values
(C) Conductor material
(D) Circuit layout

7. Which machine learning algorithm is suitable for nonlinear load prediction?
(A) Artificial Neural Network (ANN)
(B) Linear Regression
(C) Boolean Model
(D) Relay Logic

8. The support vector machine (SVM) can be used in load forecasting for:
(A) Regression and classification tasks
(B) Binary switching
(C) Frequency regulation
(D) Load disconnection

9. Fuzzy logic systems in load forecasting handle:
(A) Uncertainty in input data
(B) Purely binary outcomes
(C) Perfectly deterministic signals
(D) Only discrete data

10. Which AI model is effective for time-series load forecasting?
(A) Recurrent Neural Network (RNN)
(B) Static Regression Model
(C) Boolean Algebra
(D) PID Controller

11. The training dataset for AI-based load forecasting includes:
(A) Past consumption data and related environmental factors
(B) Random test inputs
(C) Only fixed voltages
(D) Circuit configurations

12. Deep Learning models are useful in load forecasting because they:
(A) Capture complex temporal and nonlinear relationships
(B) Only work with linear data
(C) Require no training
(D) Are limited to small datasets

13. The accuracy of load forecasting models depends heavily on:
(A) Quality and amount of training data
(B) Voltage levels
(C) Type of transformer
(D) Wire length

14. Feature selection in AI load forecasting helps to:
(A) Identify the most relevant input variables
(B) Randomize data
(C) Reduce accuracy
(D) Increase computation time

15. Which of the following is a short-term influencing factor in load forecasting?
(A) Temperature and day of the week
(B) Infrastructure growth
(C) Demographic change
(D) Policy development

16. Mean Absolute Percentage Error (MAPE) is used to:
(A) Measure load forecasting accuracy
(B) Determine transformer efficiency
(C) Calculate current losses
(D) Measure harmonics

17. Overfitting in AI forecasting models occurs when:
(A) The model fits training data too closely but performs poorly on new data
(B) The model generalizes well
(C) The dataset is too small
(D) The model ignores anomalies

18. Reinforcement Learning (RL) can be applied in load forecasting for:
(A) Adaptive and self-learning prediction systems
(B) Static scheduling
(C) Manual operation
(D) Binary control

19. The inputs to a neural network for load forecasting typically include:
(A) Load, weather, time, and calendar variables
(B) Voltage ratings only
(C) Circuit impedance
(D) Material type

20. Hybrid models combining AI methods (e.g., ANN + Fuzzy logic) improve:
(A) Accuracy and robustness of forecasts
(B) Voltage levels
(C) Manual control
(D) Energy losses

21. In seasonal load forecasting, AI models must account for:
(A) Temperature variations and seasonal trends
(B) Circuit resistance
(C) Insulation level
(D) Voltage drop

22. Which AI method is most suitable for long-term load trend prediction?
(A) Support Vector Regression (SVR)
(B) Boolean Logic
(C) PID Control
(D) Static Relay Logic

23. AI-based load forecasting reduces:
(A) Operational costs and reserve margin errors
(B) System reliability
(C) Automation
(D) Grid stability

24. The output layer in an ANN load forecasting model represents:
(A) The predicted load value
(B) Input data processing
(C) Weight initialization
(D) Noise reduction

25. Data normalization is used before training to:
(A) Scale input values and improve model convergence
(B) Increase noise
(C) Reduce dataset size
(D) Add random errors

26. Which Deep Learning architecture handles sequence-based load data best?
(A) Long Short-Term Memory (LSTM) networks
(B) Convolutional Neural Networks (CNN)
(C) Decision Trees
(D) Linear Regression

27. Feature engineering in AI load forecasting helps to:
(A) Create meaningful input variables from raw data
(B) Reduce model interpretability
(C) Increase computation time
(D) Ignore correlations

28. In smart grids, AI-based load forecasting enables:
(A) Efficient demand response and energy scheduling
(B) Manual switching
(C) Voltage increase
(D) Reactive power loss

29. The major challenge in AI-based load forecasting is:
(A) Handling large and dynamic datasets with uncertainty
(B) Reducing voltage
(C) Circuit wiring
(D) Manual data entry

30. The ultimate objective of AI in load forecasting is to:
(A) Improve accuracy, reliability, and efficiency in power system operation
(B) Eliminate automation
(C) Increase human dependency
(D) Simplify hardware design