Water heating is a major component of domestic electrical energy usage, in some countries contributing to 25% of the residential sector energy consumption. Demand response strategies can reduce the time-of-use costs and overall electrical energy consumption. We present a method to reduce the electrical energy usage itself. Our novel heating schedule control minimises the electric water heater's energy usage without compromising user convenience. We achieve optimal control, while taking into account the natural temperature stratification of the water in the tank, using the A* search algorithm. Since previous research assumes a one-node thermal model, we also assess the effect of excluding stratification. We match three optimal control strategies to a baseline: the standard “always on” thermostat control. The first two strategies respectively match the temperature and the energy of the hot water supplied by the water heater. The third, a variation on the second, includes a method of preventing the growth of Legionella bacteria. We tested 77 water heaters over four weeks, a week for each season, and all three strategies saved energy. The median savings were 6.3% for temperature-matching, 21.9% for energy-matching and 16.2% for energy-matching with Legionella prevention. Taking stratification into account increased these savings by 1.2%, 5.4% and 5.5% respectively.

水加热是家用电能使用的主要组成部分，在某些国家/地区，其耗水量占住宅部门能耗的25％。需求响应策略可以减少使用时间成本和总体电能消耗。我们提出一种减少电能本身消耗的方法。我们新颖的加热时间表控制功能可最大程度地减少电热水器的能耗，同时又不影响用户的便利性。我们使用A *搜索算法，在考虑水箱中水的自然温度分层的同时，实现了最佳控制。由于先前的研究假设一个单节点热模型，因此我们还评估了排除分层的影响。我们将三种最佳控制策略与基准进行匹配：标准的“始终开启”的恒温器控制。前两个策略分别匹配热水器提供的热水的温度和能量。第三，是第二种的变体，包括一种防止细菌生长的方法。军团菌。我们在四个星期内（每个季节一周）测试了77台热水器，所有这三种策略均节省了能源。与军团菌预防相比，温度匹配节省的中位数为6.3％，能量匹配节省的中位数为21.9％，能量匹配节省的16.2％。考虑到分层，这些节省分别增加了1.2％，5.4％和5.5％。