Abstract
Forecasting of electricity consumption for residential and industrial customers is an important task providing intelligence to the smart grid. Accurate forecasting should allow a utility provider to plan the resources as well as to take control actions to balance the supply and the demand of electricity. This paper presents two non-seasonal and two seasonal sliding window-based ARIMA (auto regressive integrated moving average) algorithms. These algorithms are developed for short-term forecasting of hourly electricity load at the district meter level. The algorithms integrate non-seasonal and seasonal ARIMA models with the OLIN (online information network) methodology. To evaluate our approach, we use a real hourly consumption data stream recorded by six smart meters during a 16-month period.
Highlights
Smart grid is becoming an increasingly popular application of the Internet of things (IoT)
Learning module it takes as input a sliding training window of a given size and calculates the parameters for a given set of ARIMA models as well as the training MAPE of each induced model. Repository of models it serves for storing the ARIMA models induced from the latest training window. Prediction module it takes as input a sliding validation window of a given size (a “prediction horizon” such as the 24 h) and calculates the validation MAPE for each ARIMA model stored in the Repository of Models
The sliding window hourly ARIMA (SWH2A) models performed significantly worse than the other models: their average validation MAPE values are about two times higher than the “naïve hourly” MAPE (11.804%)
Summary
Smart grid is becoming an increasingly popular application of the Internet of things (IoT). The smart grid includes a variety of operational and energy components connected to the Internet such as smart switches, smart meters, and smart appliances. Smart meters are aimed at monitoring and controlling household energy consumption in real time [2]. They enable two-way communication between the utility company and the customer. The proposed incremental ARIMA system is aimed at continuously processing an infinite stream of incoming data such as a series of load measurements at an hourly or daily resolution. It periodically rebuilds the predictive model using the sliding window approach. We implement two non-seasonal and two seasonal sliding window-based ARIMA algorithms and evaluate them
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