Benchmarking of Load Forecasting Methods Using Residential Smart Meter Data

As the access to consumption data available in household smart meters is now very common in several developed countries, this kind of information is assuming a providential role for different players in the energy sector. The proposed study was applied to data available from the Smart Meter Energy Consumption Data in the London Households dataset, provided by UK Power Networks, containing half-hourly readings from an original sample of 5567 households (71 households were hereby carefully selected after a justified filtering process). The main aim is to forecast the day-ahead load profile, based only on previous load values and some auxiliary variables. During this research different forecasting models are applied, tested and compared to allow comprehensive analyses integrating forecasting accuracy, processing times and the interpretation of the most influential features in each case. The selected models are based on Multivariate Adaptive Regression Splines, Random Forests and Artificial Neural Networks, and the accuracies resulted from each model are compared and confronted with a baseline (Naive model). The different forecasting approaches being evaluated have been revealed to be effective, ensuring a mean reduction of 15% in Mean Absolute Error when compared to the baseline. Artificial Neural Networks proved to be the most accurate model for a major part of the residential consumers.

Automated summary

As household smart meters become more common in developed countries, the consumption data they provide is playing a crucial role in the energy sector. This study used data from the London Households dataset to forecast the day-ahead load profile based on previous load values and auxiliary variables. Different forecasting models, including Multivariate Adaptive Regression Splines, Random Forests, and Artificial Neural Networks, were tested and compared. The results showed that the forecasting models were effective, with a mean reduction of 15% in Mean Absolute Error compared to the baseline. Artificial Neural Networks were found to be the most accurate model for the majority of residential consumers.