Discrete Sequencing for Demand Forecasting: A novel data sampling technique for time series forecasting

Abstract

Accurately forecasting energy consumption for buildings has become increasingly important over the years owing to the increasing prices of energy. A good forecast gives an understanding of how much the expected load (demand) of the building would be in the coming days and months. This could be used in further planning of energy usage within the building. This also becomes important due to the dynamic nature of energy rates. With an accurate forecast, one could also aim for spot trading by which the energy is bought and sold at different rates in a daily fashion. We target short-term and medium-term demand forecasting for buildings. Data Sampling is an integral part of training time-series models. The temporal horizon along with the patterns captured contribute to the model learning and thus its forecasts. When the data is aplenty with more than one value per day, the traditional sliding window method is unable to forecast for short-term forecasts without the actual truth values because of its continuous nature. The forecasts deviate very quickly and become unusable. In this paper, we present a novel data sampling technique called Discrete Sequencing. This samples data sequences in a lagged fashion which looks at a much larger temporal horizon with a smaller sequence size. We demonstrate the efficacy of our sampling technique by testing the forecasts on three different neural network architectures.