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Modelling and forecasting hourly electricity demand in West African countries

机译:对西非国家的每小时用电量进行建模和预测

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The Economic Community of West African States aims to achieve 100% electrification rates by 2030 in all member countries. To achieve this ambitious target, electricity generation capacities need to be increased significantly. Forecasting hourly electricity demand is imperative for capacity planners in optimizing investment options and ensuring reliable electricity supply. However, modelling hourly electricity demand in developing countries can be a challenge due to paucity of historical demand data and methodological frameworks that adequately capture technology transitions and urban-rural communities. In this study, we address this gap by developing an hourly electricity demand model for 14 West African countries in the year 2016 and 2030. The model takes into account electrification rates, available household appliances, occupancy patterns of household members, type of day, available daylight hours and hourly weather conditions. Annual electricity demand in non-residential sectors and electricity access rates in urban and rural households are forecasted using multiple regression analysis. We validated the developed model using actual 2016 monthly and annual electricity demand data. The results show the seasonal variations of electricity demand, with hourly electricity demand in dry seasons relatively higher than demand in wet seasons. The results also indicate that in 2030, electricity demand in the West African region is estimated to be five times its 2016 level. The methodology presented in this study can be applicable for modelling hourly electricity demand in developing countries that have scarce historical hourly demand data, a significant electricity supply-demand gap, and varying electricity access rates in urban and rural areas.
机译:西非国家经济共同体的目标是到2030年在所有成员国中实现100%的电气化率。为了实现这一宏伟目标,发电能力需要大大提高。对于容量规划人员来说,预测小时电力需求对于优化投资选择和确保可靠的电力供应至关重要。但是,由于对历史需求数据和方法框架的了解不足,无法充分捕捉技术转型和城乡社区的影响,因此在发展中国家对小时电力需求进行建模可能是一个挑战。在本研究中,我们通过为2016年和2030年的14个西非国家/地区开发每小时用电需求模型来解决这一差距。该模型考虑了电气化率,可用家用电器,家庭成员的居住方式,日类型,可用白天和每小时的天气状况。使用多元回归分析预测非居民部门的年电力需求和城乡居民的用电率。我们使用2016年的实际月度和年度电力需求数据验证了开发的模型。结果显示电力需求的季节性变化,干旱季节的每小时电力需求相对高于雨季的每小时电力需求。结果还表明,到2030年,西非地区的电力需求估计将是2016年水平的五倍。这项研究中介绍的方法可以适用于对历史小时需求数据稀缺,电力供需缺口较大以及城乡地区不同的用电率的发展中国家的小时电力需求进行建模。

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