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Wicksteed, Evelyn Julia

University of British Columbia

Short term load forecasting (STLF) is used by electric utility companies in their daily operations to match generation with anticipated load. Load forecasting is challenging because electricity demand is dependent on human behaviour and weather. Temperature is the weather variable most commonly used as input to STLF models. The largest electric utility in British Columbia (BC), Canada, BC Hydro, uses Vancouver temperature data as the only input to forecast load for the whole province. To better account for weather patterns across British Columbia, this research explores the use of gridded numerical weather prediction (NWP) data in multi-layer perceptron (MLP) artificial neural network (ANN) short-term load forecast models. Seven experiments are run, that differ by the source of input weather data or number of hidden layers, as follows: (1) point temperature data for Vancouver, mimicking BC Hydro’s operational model; (2) gridded temperature data for BC; (3) gridded temperature, humidity, precipitation, precipitable water, snow depth, and wind speed data for BC; (4) point temperature data for five major BC load centres: Vancouver, Victoria, Abbotsford, Kelowna, Prince George; (5) as in experiment 1, but with a two hidden layer MLP, rather than one; (6) as in experiment 2, but with a two hidden layer MLP; and (7) an ensemble method using weather model ensemble member temperature point forecasts for Vancouver. In all experiments, non-weather input variables including (a) day of the week, (b) hour of the day, (c) month, and (d) previous load values are also used. Results for both hour-ahead and 8-day forecasts show that the use of NWP data does not improve load forecast accuracy, but ensemble forecasts do. Of all seven experiments, an ensemble model (7) is the best, closely followed by a model using Vancouver point temperature data with two hidden layers (5). In both cases where two hidden layers are used in the ANN rather than one, model performance improves.

Text

2021-05-13

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/78331

Atmospheric Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/