Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation

Journal Article · Mon Feb 01 00:00:00 EST 1993 · IEEE Transactions on Power Systems (Institute of Electrical and Electronics Engineers); (United States)

DOI:https://doi.org/10.1109/59.221222· OSTI ID:6162242

Mbamalu, G A.N.; El-Hawary, M E ^[1]

---

1. Technical Univ. of Nova Scotia, Halifax, Nova Scotia (Canada)

The authors propose suboptimal least squares or IRWLS procedures for estimating the parameters of a seasonal multiplicative AR model encountered during power system load forecasting. The proposed method involves using an interactive computer environment to estimate the parameters of a seasonal multiplicative AR process. The method comprises five major computational steps. The first determines the order of the seasonal multiplicative AR process, and the second uses the least squares or the IRWLS to estimate the optimal nonseasonal AR model parameters. In the third step one obtains the intermediate series by back forecast, which is followed by using the least squares or the IRWLS to estimate the optimal season AR parameters. The final step uses the estimated parameters to forecast future load. The method is applied to predict the Nova Scotia Power Corporation's 168 lead time hourly load. The results obtained are documented and compared with results based on the Box and Jenkins method.

• OSTI ID: 6162242
• Journal Information: IEEE Transactions on Power Systems (Institute of Electrical and Electronics Engineers); (United States), Vol. 8:1; ISSN 0885-8950
• Country of Publication: United States
• Language: English

Related Subjects

29 ENERGY PLANNING
POLICY AND ECONOMY
POWER DEMAND
FORECASTING
MATHEMATICAL MODELS
HOURLY VARIATIONS
ITERATIVE METHODS
LEAST SQUARE FIT
PLANNING
REGRESSION ANALYSIS
SEASONAL VARIATIONS
CALCULATION METHODS
DEMAND
MATHEMATICS
MAXIMUM-LIKELIHOOD FIT
NUMERICAL SOLUTION
STATISTICS
VARIATIONS
292000* - Energy Planning & Policy- Supply
Demand & Forecasting
296000 - Energy Planning & Policy- Electric Power