Energy Household Forecast with ANN for Demand Response and Demand Side Management

Abstract

Abstract. This paper presents a short term load forecasting with artificial neural networks. Despite the great imprevisibility, it is possible to forecast the electricity consumption of a household with some accuracy, similarly to that the electricity utilities can do to an agglomerate of households. Nowadays, in an existing electric grid, it is important to understand and forecast household daily or hourly consumption with a reliable model for electric energy consumption and load profile. Demand response programs required this information to adequate the profile of energy load diagram to generation. In the short term load forecasting model, artificial neural networks were used, with a consumption records database. The results show that the artificial neural networks approach provides a reliable model for forecasting household electric energy consumption and load profile. To do so and using smart devices such as cyber-physical systems monitoring, gathering and computing in real time a database with weekdays and weekend, can improve forecasts results for the next hours, a strong tool for Demand Response and Demand Side Management.

Home Energy Forecast Performance Tool for Smart Living Services Suppliers under an Energy 4.0 and CPS Framework

“…The prediction of the hourly load, carried out for the next hour, next day, or next week ahead, is usually mentioned as STLF [16][17][18]. STLF plays an important role in the operation of power systems and is intended to forecast system load over a short time interval in a wide range of time leads [23].…”

“…ANN is a wellestablished machine learning technique that has been successfully applied to a large set of daily life problems, such as stock market forecasting, weather forecast, and energy consumption forecasting [25]. The ANNs provide solutions based on used previously accumulated data [16][17][18].…”

“…Inputs are the area, number of inhabitants, appliances (kitchen, entertainment), cooling, lighting, heating, and hot water. In [16,18,34,35] ANNs and a Boolean metering system are used as forecast techniques to model hourly energy consumption and load profile.…”

“…Despite the fact that power usage is variable, home consumers have a common portfolio of appliances, making it easy to establish a resemblance [12]. This paper is based on previous works, namely [16][17][18], where the authors looked to answer the challenges of energy consumption forecasting and peak load in the next hour ahead. The tools proposed by the authors use the historical databases of hourly power consumption from previous weeks, model development, and predictive algorithms based on the artificial neural networks (ANN), Fourier series (SF), and generalized reduced gradient (GRG).…”

Family Houses Energy Consumption Forecast Tools for Smart Grid Management

“…One such mechanism is DR, in which the system allows end users to alter their load shape to reduce the overall peak of the system [1]. An important tactic to DR and energy management to promote a more efficient energy end user is DSM [2]. DSM is based on a set of tools for shaping the load diagram, through peak clipping, valley filling, and shift of some loads, among others [3].…”