Abstract
Smart grid systems, which have gained much attention due to its ability to reduce operation and management costs of power systems, consist of diverse components including energy storage, renewable energy, and combined cooling, heating and power (CCHP) systems. The CCHP has been investigated to reduce energy costs by using the thermal energy generated during the power generation process. For efficient utilization of CCHP and numerous power generation systems, accurate short-term load forecasting (STLF) is necessary. So far, even though many single algorithm-based STLF models have been proposed, they showed limited success in terms of applicability and coverage. This problem can be alleviated by combining such single algorithm-based models in ways that take advantage of their strengths. In this paper, we propose a novel two-stage STLF scheme; extreme gradient boosting and random forest models are executed in the first stage, and deep neural networks are executed in the second stage to combine them. To show the effectiveness of our proposed scheme, we compare our model with other popular single algorithm-based forecasting models and then show how much electric charges can be saved by operating CCHP based on the schedules made by the economic analysis on the predicted electric loads.
Highlights
As the amount of resources consumed by one person has increased, there are growing concerns about environmental problems caused by carbon dioxide emitted during energy generation and energy shortage problems [1]
We focus on the features of the Korean power system and develop an application for scheduling CCHP operations to provide a bi-directional benefit to power suppliers and users
We proposed a novel 2-stage short-term load forecasting (STLF) model that combines popular STLF models by using a deep neural network (DNN) to further expand the domain of applicability
Summary
As the amount of resources consumed by one person has increased, there are growing concerns about environmental problems caused by carbon dioxide emitted during energy generation and energy shortage problems [1]. A smart grid is an intelligent power grid that combines information and communication technology with the existing power grid and integrates the work of all users in the power network by using computer-based remote control and automation [3]. It allows monitoring, analyzing, controlling, and communication within the supply chain to improve efficiency, reduce energy consumption and costs, and maximize the transparency and reliability of the energy supply chain [4]. By intelligentizing the power grid, it is possible to construct a bi-directional supply system such as a microgrid and distributed power supply system where suppliers and consumers can exchange information that they need [5].
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