Multi-Step Hourly Power Consumption Forecasting in a Healthcare Building with Recurrent Neural Networks and Empirical Mode Decomposition

Abstract

Short-term forecasting of electric energy consumption has become a critical issue for companies selling and buying electricity because of the fluctuating and rising trend of its price. Forecasting tools based on Artificial Intelligence have proved to provide accurate and reliable prediction, especially Neural Networks, which have been widely used and have become one of the preferred ones. In this work, two of them, Long Short-Term Memories and Gated Recurrent Units, have been used along with a preprocessing algorithm, the Empirical Mode Decomposition, to make up a hybrid model to predict the following 24 hourly consumptions (a whole day ahead) of a hospital. Two different datasets have been used to forecast them: a univariate one in which only consumptions are used and a multivariate one in which other three variables (reactive consumption, temperature, and humidity) have been also used. The results achieved show that the best performances were obtained with the multivariate dataset. In this scenario, the hybrid models (neural network with preprocessing) clearly outperformed the simple ones (only the neural network). Both neural models provided similar performances in all cases. The best results (Mean Absolute Percentage Error: 3.51% and Root Mean Square Error: 55.06) were obtained with the Long Short-Term Memory with preprocessing with the multivariate dataset.

Probabilistic Peak Demand Estimation Using Members of the Clayton Generalized Gamma Copula Family

“…To establish the parameters of each member of the CGG, one needs to equate Equation (13) to Equation ( 14) and/or Equation (15) to Equation ( 16), depending on the number of unknown parameters.…”

“…Using MAPE, Reference [14] showed that multiple linear regression seasonality models outperformed machine learning methods in predicting daily peak loads in South Korea. A multivariate hybrid prediction model using a neural network and a pre-processing algorithm incorporating reactive consumption, humidity, and temperature was used by [15] to predict the hourly electricity consumption of a hospital.…”

Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method

“…In order to capture the specific features of the electricity demand time series mentioned above, researchers have proposed various methods and models for the forecast of electricity demand over the past three decades [9][10][11][12][13][14][15][16][17][18][19]. Typically, these forecasting methods and models can be roughly divided into three categories: statistical methods, machine learning methods, and hybrid models.…”