Weather station selection for electric load forecasting

Tao Hong, Pu Wang and Laura White

International Journal of Forecasting, 2015, vol. 31, issue 2, 286-295

Abstract: Weather is a major driving factor of electricity demand. The selection of weather station(s) plays a vital role in electric load forecasting. Nevertheless, minimal research efforts have been devoted to weather station selection. In the smart grid era, hierarchical load forecasting, which provides load forecasts throughout the utility system hierarchy, is emerging as an important topic. Since there are many nodes to forecast in the hierarchy, it is no longer feasible for forecasting analysts to figure out the best weather stations for each node manually. A commonly used solution framework involves assigning the same number of weather stations to all nodes at the same level of the hierarchy. This framework was also adopted by all four of the winning teams of the Global Energy Forecasting Competition 2012 (GEFCom2012) in the hierarchical load forecasting track. In this paper, we propose a weather station selection framework to determine how many and which weather stations to use for a territory of interest. We also present a practical, transparent and reproducible implementation of the proposed framework. We demonstrate the application of the proposed approach to the forecasting of electricity at different levels in the hierarchies of two US utilities. One of them is a large US generation and transmission cooperative that has deployed the proposed framework. The other one is from GEFCom2012. In both case studies, we compare our unconstrained approach with four other alternatives based on the common practice mentioned above. We show that the forecasting accuracy can be improved by removing the constraint on the fixed number of weather stations.

Keywords: Hierarchical load forecasting; Global energy forecasting competition; Short term load forecasting; Long term load forecasting; Weather station combination; Cross validation; Out-of-sample test; Greedy algorithm (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (29)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:31:y:2015:i:2:p:286-295

DOI: 10.1016/j.ijforecast.2014.07.001

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