CAJ | 학술논문

负荷预测对电力系统的安全稳定有十分重要的作用。影响电力系统负荷短期预测的主要因素是环境，如温度、日照、湿度等，这些因素关系复杂，综合考虑所有因素会导致信息冗余，降低预测精度。笔者改进的贝叶斯分类器可以有效地对影响电力系统负荷变化的各种因素进行处理，导出其中的核心因素，以此为基础大大提高电力系统负荷预测的精度。应用此方法对某地区负荷进行了预测，结果表明该方法的有效性和准确性。
부하예측대전력계통적안전은정유십분중요적작용。영향전력계통부하단기예측적주요인소시배경，여온도、일조、습도등，저사인소관계복잡，종합고필소유인소회도치신식용여，강저예측정도。필자개진적패협사분류기가이유효지대영향전력계통부하변화적각충인소진행처리，도출기중적핵심인소，이차위기출대대제고전력계통부하예측적정도。응용차방법대모지구부하진행료예측，결과표명해방법적유효성화준학성。
Security and stability load forecasting of power system play a very important role. Factors of affecting power sys-tem short-term load forecasting are mainly environment, such as temperature, sunshine and humidity. These factors are com-plex. Considering all the factors will cause information redundancy and reducing prediction precision. Bayesian classifier im-proved author can be effectively processed all kinds of factors that affect load variation of power system, and exported its core factors. On this basis, accuracy of power system load forecasting was greatly improved. The authors forecasted load of a area by using the method. The results showed that the method had advantages of effectiveness and accuracy.