Scalable local energy management systems

Commercial buildings have been identified as a major contributor of total global energy consumption. Mechanisms for collecting data about energy consumption patterns within buildings, and their subsequent analysis to support demand estimation (and reduction) remain important research challenges, which have already attracted considerable work. We propose a cloud based energy management system that enables such analysis to scale to both increasing data volumes and number of buildings. We consider both energy consumption and storage to support: (i) flattening the peak demand of commercial building(s); (ii) enable a “cost reduction” mode where the demand of a commercial building is reduced for those hours when a “triad peak” is expected; and (iii) enables a building manager to participate in grid balancing services market by means of demand response. The energy management system is deployed on a cloud infrastructure that adapts the number of computational resources needed to estimate potential demand, and to adaptively run multiple what-if scenarios to choose the most optimum configuration to reduce building energy demand

Recent approaches and applications of non-intrusive load monitoring

The Appliance Load Monitoring is vital in every energy consuming system be it commercial, residential or industrial in nature. Traditional load monitoring system, which used to be intrusive in nature require the installation of sensors to every load of interest which makes the system to be costly, time consuming and complex. Nonintrusive load monitoring (NILM) system uses the aggregated measurement at the utility service entry to identify and disaggregate the appliances connected in the building, which means only one set of sensors is required and it does not require entrance into the consumer premises. We presented a study in this paper providing a comprehensive review of the state of art of NILM, the different methods applied by researchers so far, before concluding with the future research direction, which include automatic home energy saving using NILM. The study also found that more efforts are needed from the researchers to apply NILM in appliance energy management, for example a Home Energy Management System (HEMS)

Non-intrusive load management system for residential loads using artificial neural network based arduino microcontroller

The energy monitoring is one of the most important aspects of energy management. In fact there is a need to monitor the power consumption of a building or premises before planning technical actions to minimize the energy consumption. In traditional load monitoring method, a sensor or a group of sensors attached to every load of interest to monitor the system, which makes the system costly and complex. On the other hand, by Non-Intrusive Load Monitoring (NILM) the aggregated measurement of the building’s appliances can be used to identify and/or disaggregate the connected appliances in the building. Therefore, the method provides a simple, reliable and cost effective monitoring since it uses only one set of measuring sensors at the service entry. This thesis aims at finding a solution in the residential electrical energy management through the development of Artificial Neural Network Arduino (ANN-Arduino) NILM system for monitoring and controlling the energy consumption of the home appliances. The major goal of this research work is the development of a simplified ANN-based non-intrusive residential appliances identifier. It is a real-time ANN-Arduino NILM system for residential energy management with its performance evaluation and the calibration of the ZMPT101B voltage sensor module for accurate measurement, by using polynomial regression method. Using the sensor algorithm obtained, an error of 0.9% in the root mean square (rms) measurement of the voltage is obtained using peak-peak measurement method, in comparison to 2.5% when using instantaneous measurement method. Secondly, a residential energy consumption measurement and control system is developed using Arduino microcontroller, which accurately control the home appliances within the threshold power consumption level. The energy consumption measurement prototype has an accurate power and current measurement with error of 3.88% in current measurement when compared with the standard Fluke meter. An ANN-Arduino NILM system is also developed using steady-state signatures, which uses the feedforward ANN to identify the loads when it received the aggregated real power, rms current and power factor from the Arduino. Finally, the ANN-Arduino NILM based appliances’ management and control system is developed for keeping track of the appliances and managing their energy usage. The system accurately recognizes all the load combinations and the load controlling works within 2% time error. The overall system resulted into a new home appliances’ energy management system based on ANN-Arduino NILM that can be applied into smart electricity system at a reduced cost, reduced complexity and non-intrusively

A smart grids knowledge transfer paradigm supported by experts' throughput modeling artificial intelligence algorithmic processes

This paper presents an artificial intelligence algorithmic knowledge transfer approach to the models that have been developed throughout the world for smart grid networks. Many nations are moving forward to implement smarter ways to generate, distribute and network energy, while others are expecting the leading countries to take the initiative and then follow suit. Therefore, we theoretically identify three dimensions of experts' competencies—perception, judgment, and decision choice supported by the Throughput Model algorithms for knowledge transfer. Integrating the Throughput Model algorithmic framework and Deming Cycle (i.e., plan, do, check, act), we propose that Information and Communication Technology (ICT) systems influence experts' decision making towards implementation of Smart Grids (SG). This model was backed up with the perspectives of 32 global experts as surveyed using Carnegie Mellon Maturity model questions and analyzed the results using PLS to validate the findings and compare them to our enhanced knowledge transfer developed from Deming's PDCA cycle. Our results suggest that these key algorithmic decision-making components are critical in explaining the successful application of planning, doing, checking/ acting, and planning of renewable energy technology as well as for a greener environment

Smart home energy management: An analysis of a novel dynamic pricing and demand response aware control algorithm for households with distributed renewable energy generation and storage

Home energy management systems (HEMS) technology can provide a smart and efficient way of optimising energy usage in residential buildings. One of the main goals of the Smart Grid is to achieve Demand Response (DR) by increasing end users’ participation in decision making and increasing the level of awareness that will lead them to manage their energy consumption in an efficient way. This research presents an intelligent HEMS algorithm that manages and controls a range of household appliances with different demand response (DR) limits in an automated way without requiring consumer intervention. In addition, a novel Multiple Users and Load Priority (MULP) scheme is proposed to organise and schedule the list of load priorities in advance for multiple users sharing a house and its appliances. This algorithm focuses on control strategies for controllable loads including air-conditioners, dishwashers, clothes dryers, water heaters, pool pumps and electrical vehicles. Moreover, to investigate the impact on efficiency and reliability of the proposed HEMS algorithm, small-scale renewable energy generation facilities and energy storage systems (ESSs), including batteries and electric vehicles have been incorporated. To achieve this goal, different mathematical optimisation approaches such as linear programming, heuristic methods and genetic algorithms have been applied for optimising the schedule of residential loads using different demand side management and demand response programs as well as optimising the size of a grid connected renewable energy system. Thorough incorporation of a single objective optimisation problem under different system constraints, the proposed algorithm not only reduces the residential energy usage and utility bills, but also determines an optimal scheduling for appliances to minimise any impacts on the level of consumer comfort. To verify the efficiency and robustness of the proposed algorithm a number of simulations were performed under different scenarios. The simulations for load scheduling were carried out over 24 hour periods based on real-time and day ahead electricity prices. The results obtained showed that the proposed MULP scheme resulted in a noticeable decrease in the electricity bill when compared to the other scenarios with no automated scheduling and when a renewable energy system and ESS are not incorporated. Additionally, further simulation results showed that widespread deployment of small scale fixed energy storage and electric vehicle battery storage alongside an intelligent HEMS could enable additional reductions in peak energy usage, and household energy cost. Furthermore, the results also showed that incorporating an optimally designed grid-connected renewable energy system into the proposed HEMS algorithm could significantly reduce household electricity bills, maintain comfort levels, and reduce the environmental footprint. The results of this research are considered to be of great significance as the proposed HEMS approach may help reduce the cost of integrating renewable energy resources into the national grid, which will be reflected in more users adopting these technologies. This in turn will lead to a reduction in the dependence on traditional energy resources that can have negative impacts on the environment. In particular, if a significant proportion of households in a region were to implement the proposed HEMS with the incorporation of small scale storage, then the overall peak demand could be significantly reduced providing great benefits to the grid operator as well as the households