A comparative analysis of artificial neural network architectures for building energy consumption forecasting

Abstract

Smart grids have recently attracted increasing attention because of their reliability, flexibility, sustainability, and efficiency. A typical smart grid consists of diverse components such as smart meters, energy management systems, energy storage systems, and renewable energy resources. In particular, to make an effective energy management strategy for the energy management system, accurate load forecasting is necessary. Recently, artificial neural network–based load forecasting models with good performance have been proposed. For accurate load forecasting, it is critical to determine effective hyperparameters of neural networks, which is a complex and time-consuming task. Among these parameters, the type of activation function and the number of hidden layers are critical in the performance of neural networks. In this study, we construct diverse artificial neural network–based building electric energy consumption forecasting models using different combinations of the two hyperparameters and compare their performance. Experimental results indicate that neural networks with scaled exponential linear units and five hidden layers exhibit better performance, on average than other forecasting models.

A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling

“…However, the RICNN, which purposed a probabilistic approach, is a different purpose because we focus on day-ahead point load forecasting. In [36], the SELU-based ANN model with five hidden layers showed that the dataset we used in this study exhibited insufficient prediction accuracy compared to the other building types because its electric loads are close to zero. In [37], we proposed a two-stage electric load forecasting model to combine XGBoost and RF using MLR.…”

“…The proposed RICNN model was verified using the electric energy consumption data of three large distribution complexes in South Korea. In [36], we constructed diverse ANN models using different numbers of hidden layers and diverse activation functions and compared their performances in a 30 min STLF resolution. To compare the prediction performance, we considered electric load data collected for two years from five different types of buildings (including the dataset used in this study).…”

“…It is difficult to reflect periodic information in machine learning algorithms when data are in a sequential format. Therefore, we enhanced the data to 2-dimensional data through the periodic function [36]. Table 4 summarizes some regression statistics of 1-dimensional, 2-dimensional, and 1-dimensional + 2-dimensional time factors.…”

“…We also considered time factors, weather data, historical electric energy consumption data, and electric rate as input variables to further improve the forecasting performance. In our DNN model, we use the SELU function as an activation function and the number of hidden layers is set as five [36]. Additionally, the number of neurons in the hidden layer is set by two thirds of the number of input variables [59].…”

Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods

“…In particular, short-term load forecasting (STLF) is a core technology of the EMS [9]; moreover, accurate electric load forecasting is required for stable and efficient smart grid operations [10]. From the perspective of a supplier, it is challenging to provide optimal benefits in a cost-effective analysis while storing a large amount of electric energy in the ESS; however, the smart 2 of 37 grid can plan effectively by predicting future electric energy consumption and receiving the required energy from internal and external energy sources [11]. It is also possible to optimize the renewable energy generation process [11,12].…”