For efficient working of the power system, an accurate approach for short-term load forecasting (STLF) is suggested. To improve the accuracy of forecasting, various weather conditions, such as temperature, humidity, dew point, wind chill, and wind speed, are considered and their impact on the accuracy of load forecasting is studied in detail in terms of Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Maximum Error (ME) errors. The proposed hybrid approach consists of Support Vector Regression (SVR) and fuzzy because SVR can forecast the ability of small dataset and fuzzy system to handle non-linear weather conditions and uncertainty of load in forecasting. For load forecasting, time of the day, historical load i.e. previous one-month hourly load, weather conditions, calendar days for the last 10 days, sunny time, temperature at the same time in previous day, and average temperature of last three hours are taken into account. The proposed approach provides accurate load forecasting for a day regardless of its being a working day or holiday, while fewer days are used for load prediction viz. previous one month, while no special care is taken for weekend. The suggested approach is tested on standard electricity datasets: EUNITE network 1997 and New England of America of 2012 and 2019. Simulation results show better effectiveness and the superiority of the proposed approach when compared with other existing methods for daily load forecasting viz. ANN, Bayesian, and Least Square Support Vector Machine, etc.

为了电力系统的高效运行，提出了一种准确的短期负荷预测（STLF）方法。为了提高预报的准确性，考虑了温度、湿度、露点、风寒、风速等各种天气条件，并通过平均绝对百分比误差（Mean Absolute Percentage Error）详细研究了它们对负荷预报精度的影响（ MAPE）、均方根误差（RMSE）和最大误差（ME）误差。所提出的混合方法由支持向量回归（SVR）和模糊组成，因为SVR可以预测小数据集和模糊系统在预测中处理非线性天气条件和负载不确定性的能力。对于负载预测、一天中的时间、历史负载，即考虑前一个月的每小时负荷、天气情况、最近10天的日历天数、晴天时间、前一天同一时间的温度以及最近3小时的平均温度。所提出的方法提供了一天的准确负载预测，无论是工作日还是节假日，而用于负载预测的天数即更少。前一个月，周末没有特别照顾。建议的方法在标准电力数据集上进行了测试：1997年的EUNITE网络以及2012年和2019年的美国新英格兰。模拟结果表明，与其他现有的每日负荷预测方法（即）相比，所提出的方法具有更好的有效性和优越性。ANN、贝叶斯、最小二乘支持向量机等。