Abstract
As energy demand grows globally, the energy management system (EMS) is becoming increasingly important. Energy prediction is an essential component in the first step to create a management plan in EMS. Conventional energy prediction models focus on prediction performance, but in order to build an efficient system, it is necessary to predict energy demand according to various conditions. In this paper, we propose a method to predict energy demand in various situations using a deep learning model based on an autoencoder. This model consists of a projector that defines an appropriate state for a given situation and a predictor that forecasts energy demand from the defined state. The proposed model produces consumption predictions for 15, 30, 45, and 60 min with 60-min demand to date. In the experiments with household electric power consumption data for five years, this model not only has a better performance with a mean squared error of 0.384 than the conventional models, but also improves the capacity to explain the results of prediction by visualizing the state with t-SNE algorithm. Despite unsupervised representation learning, we confirm that the proposed model defines the state well and predicts the energy demand accordingly.
Highlights
As industrialization has progressed globally and the industry has developed, the demand for energy has become so high that energy has become an important topic in national policy [1].In addition, energy use is rapidly increasing due to economic growth and human development [2].The causes of these phenomena can be attributed to uncontrolled energy use such as overconsumption, poor infrastructure, and wastage of energy [3]
Some of the above studies provided novel research directions, other features such as information of weather and building are used in addition to the energy demand value, which is costly to construct the model for energy consumption prediction
There are many ways to deal with time series data, but f and g are based on long short-term memory (LSTM), one of the recurrent neural network (RNN)’s, to handle time series data [25,26,27,28]
Summary
As industrialization has progressed globally and the industry has developed, the demand for energy has become so high that energy has become an important topic in national policy [1]. Energy use is rapidly increasing due to economic growth and human development [2]. One work cycle of the EMS is the Plan-Do-Check-Act (PDCA) cycle as depicted in Figure 1 [6]. The “plan” phase is very important because it is the stage of establishing an energy use strategy and it includes an energy demand forecasting step. 2a, energy demand values over complex and noisy, which limits performance. For quantitative transform to analyze analyzewere patterns of energy energy demand reveals that itthis haspaper complex features. T-test and were performed on the dataset used in this paper as shown in statistical analysis using the t-test, two groups (e.g., two different months in monthly demand).
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