Abstract
Weather is a key factor affecting electricity demand. Many load forecasting models rely on weather variables. Weather stations provide point measurements of weather conditions in a service area. Since the load is spread geographically, a single weather station may not sufficiently explain the variations of the load over a vast area. Therefore, a proper combination of multiple weather stations plays a vital role in load forecasting. This paper answers the question: given a number of weather stations, how should they be combined for load forecasting? Simple averaging has been a commonly used and effective method in the literature. In this paper, we compared the performance of seven alternative methods with simple averaging as the benchmark using the data of the Global Energy Forecasting Competition 2012. The results demonstrate that some of the methods outperform the benchmark in combining weather stations. In addition, averaging the forecasts from these methods outperforms most individual methods.
Highlights
Electric load forecasting is an essential input for the decision-making processes in the power industry
We propose two other methods including a twofold combination method and a genetic algorithm (GA) based method
We assumed that the weather stations were selected by this algorithm and we only addressed weather station combination
Summary
Electric load forecasting is an essential input for the decision-making processes in the power industry. Weather-based models have been used frequently for electric load forecasting. The instruments of a weather station typically collect the information from a limited geographic area The data such as temperature or humidity reflect the weather behavior of a specific location. Multiple weather stations are inside or around the service territory, which leaves the load forecasters with the question of how to best utilize the weather data collected from different stations. Selecting and combining the temperature profiles from a group of stations is crucial to the performance of the forecasting models. In [15] each weather station was used to generate a unique load forecast and the exponentially weighted average algorithm combines the forecasts and choses the best combination based on the forecast accuracy. Despite different methods to combine weather stations, the load forecasting literature has not yet offered a formal comprehensive comparison.
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