# Electricity Consumption Forecasting for Smart Grid using the Multi-Factor Back-Propagation Neural Network

## TL;DR

This paper presents a multi-factor back-propagation neural network model for forecasting electricity consumption in smart grids, incorporating factors like weather, weekends, and holidays to improve accuracy and understanding.

## Contribution

It introduces a multi-factor BPNN model tailored for smart grid electricity forecasting, emphasizing the integration of various influencing factors for enhanced prediction accuracy.

## Key findings

- The model effectively captures the impact of weather, weekends, and holidays on electricity consumption.
- Forecasting accuracy is improved by considering multiple relevant factors.
- The approach demonstrates potential for guiding smart grid planning and management.

## 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.