Upton J, Murphy M, Shalloo L, Groot Koerkamp P W G, De Boer I J M
Animal and Grassland Research and Innovation Centre, Teagasc Moorepark Fermoy, Co. Cork, Ireland.
Dept. of Process Energy and Transport, Cork Institute of Technology, Cork, Ireland.
J Dairy Sci. 2014;97(8):4973-84. doi: 10.3168/jds.2014-8015. Epub 2014 Jun 7.
Our objective was to define and demonstrate a mechanistic model that enables dairy farmers to explore the impact of a technical or managerial innovation on electricity consumption, associated CO2 emissions, and electricity costs. We, therefore, (1) defined a model for electricity consumption on dairy farms (MECD) capable of simulating total electricity consumption along with related CO2 emissions and electricity costs on dairy farms on a monthly basis; (2) validated the MECD using empirical data of 1yr on commercial spring calving, grass-based dairy farms with 45, 88, and 195 milking cows; and (3) demonstrated the functionality of the model by applying 2 electricity tariffs to the electricity consumption data and examining the effect on total dairy farm electricity costs. The MECD was developed using a mechanistic modeling approach and required the key inputs of milk production, cow number, and details relating to the milk-cooling system, milking machine system, water-heating system, lighting systems, water pump systems, and the winter housing facilities as well as details relating to the management of the farm (e.g., season of calving). Model validation showed an overall relative prediction error (RPE) of less than 10% for total electricity consumption. More than 87% of the mean square prediction error of total electricity consumption was accounted for by random variation. The RPE values of the milk-cooling systems, water-heating systems, and milking machine systems were less than 20%. The RPE values for automatic scraper systems, lighting systems, and water pump systems varied from 18 to 113%, indicating a poor prediction for these metrics. However, automatic scrapers, lighting, and water pumps made up only 14% of total electricity consumption across all farms, reducing the overall impact of these poor predictions. Demonstration of the model showed that total farm electricity costs increased by between 29 and 38% by moving from a day and night tariff to a flat tariff.
我们的目标是定义并展示一个机制模型,使奶农能够探究技术或管理创新对电力消耗、相关二氧化碳排放以及电力成本的影响。因此,我们(1)定义了一个奶牛场电力消耗模型(MECD),该模型能够每月模拟奶牛场的总电力消耗以及相关的二氧化碳排放和电力成本;(2)使用来自商业春季产犊、以牧草为基础且分别拥有45头、88头和195头挤奶牛的奶牛场的1年经验数据对MECD进行验证;(3)通过将两种电价应用于电力消耗数据并考察其对奶牛场总电力成本的影响来展示该模型的功能。MECD是采用机制建模方法开发的,需要牛奶产量、奶牛数量以及与牛奶冷却系统、挤奶机系统、热水系统、照明系统、水泵系统和冬季住房设施相关的详细信息,以及与农场管理相关的详细信息(例如产犊季节)。模型验证表明,总电力消耗的总体相对预测误差(RPE)小于10%。总电力消耗的均方预测误差中超过87%是由随机变化造成的。牛奶冷却系统、热水系统和挤奶机系统的RPE值小于20%。自动刮板系统、照明系统和水泵系统的RPE值在18%至113%之间变化,表明对这些指标的预测较差。然而,自动刮板、照明和水泵在所有农场的总电力消耗中仅占14%,从而降低了这些不佳预测的总体影响。该模型的展示表明,从昼夜电价改为统一电价后,农场的总电力成本增加了29%至38%。