Abstract
As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to adjust the parameters of the first particle swarm optimization algorithm to enhance the accuracy of the forecasting results. The models were tested with actual data collected from the Tuy Phong wind power plant in Binh Thuan Province, Vietnam. The testing showed improved accuracy and that this model can be widely implemented at other wind farms.
Highlights
Along with the requirements of the process of industrialization and modernization, together with increasing demands for economic growth and the exchange of goods and services across the globe, ensuring sufficient sources of energy has posed many challenges to countries worldwide
By means of an open-source Python programming language, a wind power forecasting program was built with a hybrid application of an artificial neural network, particle swarm optimization algorithm, and genetic algorithm
The two models were successfully applied for the forecasting power output of the Tuy Phong wind power plant in Binh Thuan Province, Vietnam
Summary
Along with the requirements of the process of industrialization and modernization, together with increasing demands for economic growth and the exchange of goods and services across the globe, ensuring sufficient sources of energy has posed many challenges to countries worldwide. Traditional sources of energy such as coal, oil, and gas are increasingly exhausted, cause environmental pollution and uplift the greenhouse effects. To solve this problem, renewable energy has been widely encouraged with several alternative sources of energy being introduced and continuously developed. Renewable energy has been widely encouraged with several alternative sources of energy being introduced and continuously developed One of these is wind power, which is referred to as a clean source of energy with great potential for development. With the pace as it is wind power will soon occupy a large portion of the world energy market. According to the Global Wind Report 2019 (GWEC), 60.4 GW of new installations brings the global cumulative wind power capacity up to 651 GW, as shown in Figure 1 [1]
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