Recently, multistep-ahead prediction has attracted much attention in electric load forecasting because it can deal with sudden changes in power consumption caused by various events such as fire and heat wave for a day from the present time. On the other hand, recurrent neural networks (RNNs), including long short-term memory and gated recurrent unit (GRU) networks, can reflect the previous point well to predict the current point. Due to this property, they have been widely used for multistep-ahead prediction. The GRU model is simple and easy to implement; however, its prediction performance is limited because it considers all input variables equally. In this paper, we propose a short-term load forecasting model using an attention based GRU to focus more on the crucial variables and demonstrate that this can achieve significant performance improvements, especially when the input sequence of RNN is long. Through extensive experiments, we show that the proposed model outperforms other recent multistep-ahead prediction models in the building-level power consumption forecasting.

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.

Load forecasting is one of the critical tasks for enhancing the energy efficiency of smart grids. Even though recent deep learning-based load forecasting models have shown excellent forecasting performance, one of the common problems they faced was that their forecasting accuracy was highly dependent on the data quality and quantity available for the model training. Collecting a sufficient amount of high-quality data is expensive and time-consuming. Recently, a generative adversarial network (GAN) has shown its potential as a solution to the data shortage problem by generating virtual data based on a small amount of real data, and several studies have used GAN to generate electric load data for training forecasting models. However, due to the noise data problem of GANs, their predictive performance also deteriorated. To solve this problem, in this study, we propose a two-stage data generation scheme that more effectively generates input and output variables for short-term load forecasting. In the first stage, we generate virtual calendar and temperature data used as input variables using a conditional tabular GAN (CTGAN). In the second stage, we generate electric load data corresponding to the input variables using a deep learning-based regression model. Lastly, we construct our forecasting model by training another regression model using a mixture of generated data and real data. To verify the effectiveness of our scheme, we conducted extensive experiments using various datasets and data generation models. We report some of the results.

Monthly electric load forecasting is essential to efficiently operate urban power grids. Although diverse forecasting models based on artificial intelligence techniques have been proposed with good performance, they require sufficient datasets for training. In the case of monthly forecasting, because just one data point is generated per month, it is not easy to collect sufficient data to construct models. This lack of data can be alleviated using transfer learning techniques. In this paper, we propose a novel monthly electric load forecasting scheme for a city or district based on transfer learning using similar data from other cities or districts. To do this, we collected the monthly electric load data from 25 districts in Seoul for five categories and various external data, such as calendar, population, and weather data. Then, based on the available data of the target city or district, we selected similar data from the collected datasets by calculating the Pearson correlation coefficient and constructed a forecasting model using the selected data. Lastly, we fine-tuned the model using the target data. To demonstrate the effectiveness of our model, we conducted an extensive comparison with other popular machine-learning techniques through various experiments. We report some of the results.

For efficient and effective energy management, accurate energy consumption forecasting is required in energy management systems (EMSs). Recently, several artificial intelligence-based techniques have been proposed for accurate electric load forecasting; moreover, perfect energy consumption data are critical for the prediction. However, owing to diverse reasons, such as device malfunctions and signal transmission errors, missing data are frequently observed in the actual data. Previously, many imputation methods have been proposed to compensate for missing values; however, these methods have achieved limited success in imputing electric energy consumption data because the period of data missing is long and the dependency on historical data is high. In this study, we propose a novel missing-value imputation scheme for electricity consumption data. The proposed scheme uses a bagging ensemble of multilayer perceptrons (MLPs), called softmax ensemble network, wherein the ensemble weight of each MLP is determined by a softmax function. This ensemble network learns electric energy consumption data with explanatory variables and imputes missing values in this data. To evaluate the performance of our scheme, we performed diverse experiments on real electric energy consumption data and confirmed that the proposed scheme can deliver superior performance compared to other imputation methods.