Arizona-based APS Energy Services (Apses) has already started using PerfecTemp Hourly to incorporate into its energy load forecasting and demand volatility risk analysis.
“We are impressed with AIR-Weather’s temperature data in terms of its integrity and completeness,” says Brian Ochs, climate data consultant at Apses. “PerfecTemp Hourly has greatly helped us in resolving the numerous missing data gaps we have faced with datasets acquired from other sources.”
AIRWeather developed PerfecTemp to improve data quality to fill a large void in the industry. Common problems with raw data include erroneous values, data gaps, station shifts, instrument changes and environmental effects, all of which adversely affect the quality of data.
To address these problems, AIRWeather developed new data-cleaning methodologies based on the original research of its meteorological team. The cleaning process involves identifying data gaps and erroneous values and replacing these with estimates.
After the data is cleaned, the reconstruction process corrects for time-series shifts and trends that are the result of non-weather-related factors, such as station moves, instrument changes and other environmental changes. All cleaned and reconstructed data is then tested for accuracy and quality.