Abstract
Energy efficiency and renewable energy are the two main research topics for sustainable energy. In the past ten years, countries around the world have invested a lot of manpower into new energy research. However, in addition to new energy development, energy efficiency technologies need to be emphasized to promote production efficiency and reduce environmental pollution. In order to improve power production efficiency, an integrated solution regarding the issue of electric power load forecasting was proposed in this study. The solution proposed was to, in combination with persistence and search algorithms, establish a new integrated ultra-short-term electric power load forecasting method based on the adaptive-network-based fuzzy inference system (ANFIS) and back-propagation neural network (BPN), which can be applied in forecasting electric power load in Taiwan. The research methodology used in this paper was mainly to acquire and process the all-day electric power load data of Taiwan Power and execute preliminary forecasting values of the electric power load by applying ANFIS, BPN and persistence. The preliminary forecasting values of the electric power load obtained therefrom were called suboptimal solutions and finally the optimal weighted value was determined by applying a search algorithm through integrating the above three methods by weighting. In this paper, the optimal electric power load value was forecasted based on the weighted value obtained therefrom. It was proven through experimental results that the solution proposed in this paper can be used to accurately forecast electric power load, with a minimal error.
Highlights
Due to human over-exploitation, the global warming and energy crisis is a challenge that human beings must face
The research methodology used in this paper was mainly to acquire and process the all-day electric power load data of Taiwan Power and execute preliminary forecasting values of the electric power load by applying adaptive-network-based fuzzy inference system (ANFIS), back-propagation neural network (BPN) and persistence
The preliminary forecasting values of the electric power load obtained therefrom were called suboptimal solutions and the optimal weighted value was determined by applying a search algorithm through integrating the above three methods by weighting
Summary
Due to human over-exploitation, the global warming and energy crisis is a challenge that human beings must face. Power forecasting is important for power companies to improve energy efficiency. Whether the load demand is greater than the power supply capacity caused by a power jump, or the energy waste caused by an oversupply of electricity, the power cost for the power company will increase. The demand for electric power has been growing steadily along with booming economic growth in Taiwan. A stable electric power supply should be the basis for national economic development. A noticeable increase in demand for industrial and civil electricity has been observed due to rapid economic development. Electric power companies can reduce electric power operation costs and further improve the quality and stability of the electric power supply if the future load can be accurately forecasted
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