Demand Based Cost Optimization of Electric Bills for Household Users

Abstract- Internet of Things (IoT) is increasingly becoming the vehicle to automate, optimize and enhance the performance of systems in the energy, environment, and health sectors. In this paper, we use Wi-Fi wrapped sensors to provide online and in realtime the current energy consumptions at a device level, in a manner to allow for automatic control of peak energy consumption at a household, factory level, and eventually at a region level, where a region can be defined as an area supported by a distinct energy source. This allows to decrease the bill by avoiding heavily and controllable loads during high tariff slice and/or peak period per household and to optimize the energy production and distribution in a given region. The proposed model relies on adaptive learning techniques to help adjust the current load, while taking into consideration the actual and real need of the consumer. The experiments used in this study makes use of current and voltage sensors, Arduino platform, and simulation system. The main performance indexes used are the control of a peak consumption level, and the minimum time needed to adjust the distribution of load in the system. The system was able to keep the maximum load at a maximum of 10 kW in less than 10 seconds of response time. The level and response time are controllable parameters

Realistic Multi-Scale Modelling of Household Electricity Behaviours

To improve the management and reliability of power distribution networks, there is a strong demand for models simulating energy loads in a realistic way. In this paper, we present a novel multi-scale model to generate realistic residential load profiles at different spatial-temporal resolutions. By taking advantage of information from Census and national surveys, we generate statistically consistent populations of heterogeneous families with their respective appliances. Exploiting a Bottom-up approach based on Monte Carlo Non Homogeneous Semi-Markov, we provide household end-user behaviours and realistic households load profiles on a daily as well as on a weekly basis, for either weekdays and weekends. The proposed approach overcomes limitations of state-of-art solutions that do not consider neither the time-dependency of the probability of performing specific activities in a house, nor their duration, or are limited in the type of probability distributions they can model. On top of that, it provides outcomes that are not limited on a per-day basis. The range of available space and time resolutions span from single household to district and from second to year, respectively, featuring multi-level aggregation of the simulation outcomes. To demonstrate the accuracy of our model, we present experimental results obtained simulating realistic populations in a period covering a whole calendar year and analyse our model’s outcome at different scales. Then, we compare such results with three different data-sets that provide real load consumption at household, national and European levels, respectively