Evolving smart meter data driven model for short-term forecasting of electric loads

Abstract

Short-term forecasting of electric loads is an essential function required by Smart Grids. Today increasing amount of smart metering data is available enabling the development of enhanced data-driven models for short-term load forecasting. Until now, a plethora of models have been developed ranging from simple linear regression models to more advanced models such as (artificial) neural networks (NNs) and support vector machines (SVMs). Despite the relatively high accuracy obtained, the acceptance of purely data-driven models such as NN models is still remained limited due to their complexity and nontransparent nature. Therefore it is important to develop optimization schemes, which can be used to facilitate the selection of appropriate model structure resulting good forecasting accuracy with low complexity. This study presents an optimization scheme based on multi-objective genetic algorithm (GA) for designing data-driven models for short-term forecasting of electric loads. The optimization scheme is demonstrated for designing the conventional NN/MLP model using real smart metering data and weather measurements. The optimal NN model structures are identified and analyzed in terms of model complexity and forecasting accuracy.

Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

“…Our choice of models for comparison is based on the wide use of these classification techniques, SVM and MLP, and wide acceptance as two well-known approaches for classification and prediction by the research community [53][54][55][56][57][58][59][60][61][62][63][64].…”

Mining smart meter data - case Finland

“…During the SGEM project, several different models utilizing hourly metered consumption data were evaluated for shortterm load forecasting. The studied models were; a cluster profile based predictor, a Kalman-filter based predictor with input nonlinearities and physically based main structure, a neural network (NN) model [4], and a support vector machine (SVM) model [8]. The NN and SVM models were the most accurate, but also the other methods had their relative merits.…”