Abstract
Peak-load forecasting prevents energy waste and helps with environmental issues by establishing plans for the use of renewable energy. For that reason, the subject is still actively studied. Most of these studies are focused on improving predictive performance by using varying feature information, but most small industrial facilities cannot provide such information because of a lack of infrastructure. Therefore, we introduce a series of studies to implement a generalized prediction model that is applicable to these small industrial facilities. On the basis of the pattern of load information of most industrial facilities, new features were selected, and a generalized model was developed through the aggregation of ensemble models. In addition, a new method is proposed to improve prediction performance by providing additional compensation to the prediction results by reflecting the fewest opinions among the prediction results of each model. Actual data from two small industrial facilities were applied to our process, and the results proved the effectiveness of our proposed method.
Highlights
Peak-load forecasting prevents the waste of energy and with helps environmental issues by establishing plans for the use of renewable energy
To verify that the our proposed algorithm could successfully lead to peak-load forecasting for small-scale industrial facilities with limited information, we outline a series of experiments that we carried out
We analyzed patterns of hourly power consumption in small-scale industrial facilities where peak loads are highly dependent on daily production, and we selected new feature information to effectively predict daily peak load
Summary
Peak-load forecasting prevents the waste of energy and with helps environmental issues by establishing plans for the use of renewable energy. Building automation and energy management systems provides reliable peak-load prediction by offering a variety of information to the model It is difficult for most small-scale industrial facilities to provide all input variables desired by this prediction model. We propose a peak-load prediction model for small industrial facilities that provide only hourly power consumption information. We propose a new feature selection method that makes it possible to predict daily peak power by finding the load pattern for a short period of time from the time the facility is started. We propose an ensemble model, so that the peak-load-forecasting method for small industrial facilities could be extended in general cases.
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