Abstract
A stable power supply is very important in the management of power infrastructure. One of the critical tasks in accomplishing this is to predict power consumption accurately, which usually requires considering diverse factors, including environmental, social, and spatial-temporal factors. Depending on the prediction scope, building type can also be an important factor since the same types of buildings show similar power consumption patterns. A university campus usually consists of several building types, including a laboratory, administrative office, lecture room, and dormitory. Depending on the temporal and external conditions, they tend to show a wide variation in the electrical load pattern. This paper proposes a hybrid short-term load forecast model for an educational building complex by using random forest and multilayer perceptron. To construct this model, we collect electrical load data of six years from a university campus and split them into training, validation, and test sets. For the training set, we classify the data using a decision tree with input parameters including date, day of the week, holiday, and academic year. In addition, we consider various configurations for random forest and multilayer perceptron and evaluate their prediction performance using the validation set to determine the optimal configuration. Then, we construct a hybrid short-term load forecast model by combining the two models and predict the daily electrical load for the test set. Through various experiments, we show that our hybrid forecast model performs better than other popular single forecast models.
Highlights
The smart grid has been gaining much attention as a feasible solution to the current global energy shortage problem [1]
We proposed a hybrid model for short-term load forecasting for higher educational building remodeling and construction
Even though the remodeling and construction are finished at institutions, such as universities, using random forest and multilayer perceptron
Summary
The smart grid has been gaining much attention as a feasible solution to the current global energy shortage problem [1]. (STLF) aims to prepare for losses caused by energy failure and overloading by maintaining an active power consumption reserve margin [5] It includes daily electrical load, highest or peak electrical load, and very short-term load forecasting (VSTLF). By considering power consumption patterns and various external factors together, many machine learning algorithms have shown a reasonable performance in short-term load forecasting [3,4,6,8,14,15,16,17]. Since the campus usually remains closed on the holidays, the power consumption of the campus becomes very low In such cases, it is difficult for a single excellent algorithm to make accurate predictions for all patterns.
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