Forecasting one-step-ahead higher order statistical moments and probability density functions for power system loads using least square estimators
Conventional load forecasting involves the prediction of the expected value of the demand of an electric power system. The expected value of a quantity which is subject to uncertainty does not fully characterize that quantity. In this thesis the one-step-ahead load value is calculated using a least square estimator which relies on previous load measurements. Subsequently, the load values are treated as an ensemble of random variables with calculable statistical moments. Calculating these moments will enable one to predict the entire one-step-ahead probability density function of the load using Gram-Charlier series Type A. From this density function, a wide variety of statistical quantities may be calculated: the mean value, the probability that the load will exceed some threshold, conditional probabilities (under special conditions such as negative generation margin), and conditional expectations.
- Research Organization:
- Purdue Electric Power Center, Lafayette, IN (USA); Purdue Univ., Lafayette, IN (USA). School of Electrical Engineering
- DOE Contract Number:
- AS02-77ET29102
- OSTI ID:
- 5259178
- Report Number(s):
- PCTR-94-80; TR-EE-80-19
- Country of Publication:
- United States
- Language:
- English
Similar Records
Stochastic optimal energy dispatch
Generalized stochastic power flow algorithm
Related Subjects
24 POWER TRANSMISSION AND DISTRIBUTION
29 ENERGY PLANNING, POLICY, AND ECONOMY
296000* -- Energy Planning & Policy-- Electric Power
DATA
FORECASTING
INFORMATION
LOAD MANAGEMENT
MANAGEMENT
MATHEMATICAL MODELS
NUMERICAL DATA
POWER DEMAND
POWER SYSTEMS
PROBABILITY
STATISTICAL MODELS
THEORETICAL DATA