Short-Term Load Forecasting Questions and Answers

Part 1: Multiple Choice 1. What is identified as the principal objective of Short-Term Load Forecasting (STLF)? Answer: To provide load prediction for generation scheduling and system security. 2. In the context of STLF, which factor is considered the MOST influential weather factor? Answer: Temperature. 3. Which component of a biological neuron is modeled by the 'weight vector' in an Artificial Neural Network (ANN)? Answer: Synapses. 4. The proposed ANN architecture in the chapter uses three separate seasonal networks. Which two seasons are combined into a single network due to similar load patterns? Answer: Spring and Fall. 5. What is the primary purpose of the 'adaptive weight' procedure described in the chapter? Answer: To update the ANN weights regularly based on the latest load information. 6. According to the sensitivity analysis, which input factor has the LARGEST impact on the forecasting accuracy (MAPE)? Answer: Historical load. 7. What is a key disadvantage of using a single ANN model for all seasons (the Alt1 model) compared to the proposed seasonal model? Answer: It shows poorer performance, especially under extreme weather conditions. 8. The 'effective temperature' (Tc) concept is introduced to: Answer: Implicitly include the effects of humidity and wind without adding more input neurons. 9. In the chapter, STLF is categorized based on duration. Which category is used for Automatic Generation Control (AGC)? Answer: Very short-term load forecasting (minutes ahead). 10. What is the main reason given for not recommending load forecasts beyond seven days with the multiple-day forecast strategy? Answer: Cumulative errors and unreliable long-term temperature forecasts. Part 2: True or False 1. Statement: In a restructured power system, STLF is only important for the system operator (ISO). · True/False: False · Justification: STLF is also important for GENCOs, TRANSCOs, and DISCOs in market operations. 2. Statement: The "load shape model" treats load as a function of time, unlike the "peak load model." · True/False: True · Justification: The load shape model depends on time while the peak load model does not. 3. Statement: A Kohonen network is an example of a supervised learning ANN architecture. · True/False: False · Justification: A Kohonen network is an unsupervised learning ANN. 4. Statement: The proposed ANN architecture uses 24 neurons in its output layer to represent the forecasted load for each hour of the day. · True/False: True · Justification: The model has 24 output neurons, one for each hour of the day. 5. Statement: The stopping criterion for the ANN training process in the chapter is a fixed number of training hours. · True/False: False · Justification: Training stops when the forecast error (MAPE) is less than 3%, not by time. 6. Statement: The Alt2 model combines the forecasts from two separate networks: a Base Load Forecast (BLF) and a Change Load Forecast (CLF). · True/False: True · Justification: Alt2 combines BLF and CLF outputs to improve accuracy. 7. Statement: According to the sensitivity analysis, including more weather factors like humidity and wind always significantly improves forecasting accuracy, regardless of the cost. · True/False: False · Justification: Adding more weather factors does not always improve accuracy and increases cost. 8. Statement: The MAPE (Mean Absolute Percentage Error) is used in the chapter to evaluate the performance of the load forecasting models. · True/False: True · Justification: MAPE is used to measure forecasting accuracy. 9. Statement: The partitioning method in ANN for STLF involves using the same network for all days and seasons without any separation. · True/False: False · Justification: The method uses separate networks for each season and type of day. 10. Statement: The performance results show that the proposed seasonal ANN model consistently had a higher average MAPE than the Alt1 model. · True/False: False · Justification: The proposed seasonal ANN had a lower MAPE, showing better accuracy than Alt1. Part 3: Fill in the Blank 1. For thermal power systems, STLF is used for scheduling __________, which involves calculating the start-up and shut-down of each plant. Answer: Unit commitment. 2. Sudden load changes can be caused by __________ disturbances, such as the operation of a large industrial plant or a popular TV event. Answer: Random disturbances. 3. The mathematical model of an artificial neuron includes an input vector, a weight vector, an adder, a transfer function, and an _________ Answer: Output vector (axon). 4. In a multi-layer perceptron, the layer that has no direct connection to the outside world is called the _________ layer. Answer: Hidden layer. 5. The proposed ANN architecture uses a total of __________ neurons in its input layer. Answer: 73 neurons. 6. The process of updating ANN weights daily based on the difference between actual and forecasted loads is known as __________. Answer: Adaptive weight (or weight adaptation) 7. In the multiple-day forecast strategy, the load forecast for day d is used as the "previous day load" input for the forecast of day __________ Answer: d+1 8. The test set for the winter model in the numerical experiments was the load data for the month of __________. Answer: December. 9. Model 5 in the sensitivity analysis uses the concept of __________ temperature to implicitly include the effects of humidity and wind. Answer: Effective temperature 10. The two main learning strategies for ANNs are supervised learning and __________ learning. Answer: Unsupervised learning Part 4: Matching Type 1. GENCO Answer: Entity that operates and maintains generating plants in a restructured market. 2. Hopfield Network Answer: A feedback ANN architecture where each neuron's output is connected to others. 3. Fuzzy Logic Answer: Methodology used in hybrid systems with ANN to handle imprecise inputs like 'low' or 'high' temperature. 4. ARMA Model Answer: Model that includes an additive load residual term to describe influences from weather deviations. 5. MATLAB ANN Toolbox Answer: Software environment used for designing and training custom neural networks. 6. State-Space Model Answer: Model that assumes the parameters defining the periodic component of the load are random processes. 7. Arbitrage Answer: Making a profit by simultaneously buying and selling the same commodity in different markets. 8. Market Power Answer: The ability of a seller to influence the market price above competitive levels. 9. PoolCo Model Answer: A centralized market that clears energy for buyers and sellers based on aggregated supply and demand bids. 10. Bilateral Contract Answer: A negotiable agreement between two parties for the delivery and receipt of power, independent of the ISO. Part 5: Short Answer 1. Besides generation scheduling, name TWO other applications of STLF. Answer: Besides generation scheduling, STLF is also used for power system security to help operators predict and prevent problems in the grid. It is also applied in determining the generation reserve needed to ensure enough power supply. These uses help maintain system reliability and stability. 2. What is the key structural difference between the proposed seasonal ANN and the Alt1 model? Answer: . The key structural difference is that the proposed seasonal ANN uses three separate networks for summer, winter, and spring/fall. The Alt1 model only uses one single network for all seasons. Using separate seasonal networks improves accuracy and makes training easier.