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Electricity Demand Forecast of College of Science and Technology, Royal University of Bhutan by 2030

机译:2030年皇家皇家大学皇家大学理工学院电力需求预测

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Increase in the number of students and staff in the campus has led to increase in the consumption of electricity from the gird. It is important to have reliable electricity plan to meet the future needs and to become self-sufficient. This paper presents a forecast of the electricity demand of the College of Science and Technology until 2030. The historical electricity consumption data from January 2014 until December 2018 was used for the forecast. The future electricity consumption was forecasted using Autoregressive Integrated Moving Average (ARIMA) model in XLSTAT. ARIMA was specified by three order parameters (p, d, q). To identify the model of ARIMA, the autocorrelation function (ACF), and partial autocorrelation function (PACF) were used. The efficiency of the model was checked using root mean square error (RMSE), mean square error (MSE), and the sum of square error (SSE). The forecast was also validated using the best fit comparison of raw data with the predicted data. The total electricity consumption of the college is forecast to increase from 1.09 MWh in 2018 to 5.75 MWh in 2030 with an average increase of 14.67 % per year. Similarly, electricity consumption in the staff residential zone is projected to increase from 166 MWh in 2018 to 295 MWh in 2030. In the case of student residential zone, the electricity consumption is forecast to increase from 273 MWh in 2018 to 361 MWh by 2030.
机译:校园中的学生和工作人员的数量增加导致了从凝剑的电力消耗增加。重要的是要拥有可靠的电力计划,以满足未来的需求并变得自给自足。本文介绍了科学技术学院的电力需求,直到2030年。2014年1月至2018年12月的历史电力消费数据用于预测。使用XLSTAT中的自回归综合移动平均(ARIMA)模型预测未来的电力消耗。 Arima由三个订单参数(p,d,q)指定。为了识别Arima的模型,使用自相关函数(ACF)和部分自相关函数(PACF)。使用根均方误差(RMSE),均方误差(MSE)以及平方误差(SSE)的总和检查模型的效率。使用预测数据的最佳拟合比较还验证了预测。预计大学的电力消费量预计将从2018年的1.09兆瓦时增加到2030年的5.75兆瓦,平均每年增加14.67%。同样,员工住宅区的电力消耗预计将从2018年的166兆瓦行增加到2030年的295兆瓦。在学生住宅区,预计电力消耗将从2018年的273兆瓦时增加到2030年。

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