Electricity consumption forecasting for smart grid using the multi-factor back-propagation neural network

Abstract

With the development of modern information technology (IT), a smart grid has become one of the major components of smart cities. To take full advantage of the smart grid, the capability of intelligent scheduling and planning of electricity delivery is essential. In practice, many factors have an impact on electricity consumption, which necessitates information fusion technologies for a thorough understanding. For this purpose, researchers have investigated methodologies for collecting electricity consumption related information and variant multi-factor power consumption forecasting models. In addition, conducting a comprehensive analysis and obtaining an accurate evaluation of power consumption are the premise and basis for a more robust and efficient power grid design and transformation. Therefore, it is meaningful to explore forecasting models that are able to reflect the power consumption changes and internal relations within fusional information effectively. Making electricity consumption forecasting based on the neural network has been a popular research topic in recent years, and the back-propagation neural network (BPNN) algorithm has been recognized as a mature and effective method. In this paper, BPNN is adopted to forecast the electricity consumption using Pecan Street, a community with a relatively large-scale smart grid, as a case study, and takes multiple factors into account, such as weather condition, weekend and holidays. The influences of each factor have been evaluated for a deeper insight. We hope this work will inspire more discussion and further study to guide the design of future smart grids.

Study of Structural Relationships and Models of Information Support for Energy Life Cycle Management in Microgrid

“…The vast majority of neural network models are effective if the inputs are properly selected and the models are configured. In [20], when using a neural network of the BPNN type for short-term prediction of power consumption, more than 90% is achieved, and it increases when the model of meteorological factors is included in the inputs. A study of the use of the NARX neural network for mediumterm prediction of power consumption conducted on a data set by the University of Malaysia showed that the accuracy for the NARX model is approximately 98%, which is more accurate than for time series, fuzzy time series and multiple linear regressions [21].…”