Autonomous residential load control based on electricity price prediction - Sze, Desmond Jun Jie (2018)
Real-time electricity pricing models can possibly bring about economic and environment-friendly benefits in comparison with typical flat rates at the present. These models can give end users a chance to cut down their electricity costs by reacting to electricity prices that differs at various timings of the day. However, researches have shown that the two major barricades for fully maximizing the potential advantages of real-time pricing tariffs are 1) The inadequate awareness among users on how to react to time-changing prices and 2) The inadequate provision of efficient building automation systems. It is often said that any residential load control approach in real-time electricity pricing conditions requires price prediction proficiency. This is especially true if utility companies can come up with information with regards to price at one or two hours beforehand. The solution to these problems would be to come up with an optimal and automatic residential energy consumption scheduling framework that strives to accomplish a wanted trade-off between reducing electricity costs and reducing the time spent to wait for each household appliance to operate using a real-time pricing tariff incorporated with inclining block rates. The framework is created in such a way that it only needs minimal effort from the users and it runs on non-complicated linear programming permutations.