Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy

Abstract

Electric power consumption short-term forecasting for individual households is an important and challenging topic in the fields of AI-enhanced energy saving, smart grid planning, sustainable energy usage and electricity market bidding system design. Due to the variability of each household’s personalized activity, difficulties exist for traditional methods, such as auto-regressive moving average models, machine learning methods and non-deep neural networks, to provide accurate prediction for single household electric power consumption. Recent works show that the long short term memory (LSTM) neural network outperforms most of those traditional methods for power consumption forecasting problems. Nevertheless, two research gaps remain as unsolved problems in the literature. First, the prediction accuracy is still not reaching the practical level for real-world industrial applications. Second, most existing works only work on the one-step forecasting problem; the forecasting time is too short for practical usage. In this study, a hybrid deep learning neural network framework that combines convolutional neural network (CNN) with LSTM is proposed to further improve the prediction accuracy. The original short-term forecasting strategy is extended to a multi-step forecasting strategy to introduce more response time for electricity market bidding. Five real-world household power consumption datasets are studied, the proposed hybrid deep learning neural network outperforms most of the existing approaches, including auto-regressive integrated moving average (ARIMA) model, persistent model, support vector regression (SVR) and LSTM alone. In addition, we show a k-step power consumption forecasting strategy to promote the proposed framework for real-world application usage.

Accurate Deep Model for Electricity Consumption Forecasting Using Multi-Channel and Multi-Scale Feature Fusion CNN–LSTM

“…Multi-step forecasting can provide multi-step future consumption information in advance. To validate the multi-step forecasting capacity of the proposed method for multiple forecasts, we designed a five-step forecasting experiment and compared it to [27], which tests the multi-step forecasting capacity of their model for VSTF. The input of the proposed MCSCNN-LSTM for multi-step forecasting in Figure 2 has the same shape with one-step forecasting, we only need to change the output into one vector with five elements regarding the five future consumption data points of each forecast.…”

“…However, collecting such correlated variables is hard and time-consuming in reality. Although Yan et al proposed a hybrid of CNN-LSTM to predict power consumption by using raw time series, it only focused on VSTF (minutely) [27]. Moreover, Yan et al [28] proposed a hybrid LSTM model, in which wavelet transform (WT) is applied to preprocess the raw univariate time series firstly.…”

Special Issue “Intelligent Control in Energy Systems”

“…A hybrid deep learning neural network framework that combined Convolutional Neural Network (CNN) with Long Short Term Memory (LSTM) with a multi-step forecasting strategy was proposed in [14] in order to fill the research gaps that existed in power consumption forecasting problems and were considered as disadvantages in practical applications of LSTM: The prediction's accuracy and the shortness of the forecasting time. The proposed framework was tried against some of the known existing approaches, such as ARIMA, persistent model, SVR, and LSTM alone.…”

An Online Hybrid Model for Temperature Prediction of Wind Turbine Gearbox Components

“…Four elements, including state of each unit, input gate, forget gate and output gate, are the core of the LSTM model. The relationship of the LSTM unit states and the three gates are expressed as Equations (1)-(5) [34,35].…”