Nearest neighbors with incremental learning for real-time forecasting of electricity demand

Electricity demand forecasting is very useful for the different actors involved in the energy sector to plan the supply chain (generation, storage and distribution of energy). Nowadays energy demand data are streaming data coming from smart meters and has to be processed in real-time for more efficient demand management. In addition, this kind of data can present changes over time such as new patterns, new trends, etc. Therefore, real-time forecasting algorithms have to adapt and adjust to online arriving data in order to provide timely and accurate responses. This work presents a new algorithm for electricity demand forecasting in real-time. The proposed algorithm generates a prediction model based on the K-nearest neighbors algorithm, which is incrementally updated as online data arrives. Both time-frequency and error threshold based model updates have been evaluated. Results using Spanish electricity demand data with a ten-minute sampling frequency rate are reported, reaching 2% error with the best prediction model obtained when the update is daily.