Abstract
Effective and efficient use of energy is key to sustainable industrial and economic growth in modern times. Demand-side management (DSM) is a relatively new concept for ensuring efficient energy use at the consumer level. It involves the active participation of consumers in load management through different incentives. To enable the consumers for efficient energy management, it is important to provide them information about the energy consumption patterns of their appliances. Appliance load monitoring (ALM) is a feedback system used for providing feedback to customers about their power consumption of individual appliances. For accessing appliance power consumption, the determination of the operating status of various appliances through feedback systems is necessary. Two major approaches used for ALM are intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM). In this paper, a hybrid adaptive-neuro fuzzy inference system (ANFIS) is used as an application for NILM. ANFIS model being sophisticated was difficult to work with, but ANFIS model helps to achieve better results than other competent approaches. An ANFIS system is developed for extracting appliance features and then a fine tree classifier is used for classifying appliances having more than 1 kW power rating based on the extracted feature. Several case studies have been performed using ANFIS on a publicly available United Kingdom Domestic Appliance Level Electricity (UK-Dale dataset). The simulation results obtained from the ANFIS for NILM are compared with relevant literature to show the performance of the proposed technique. The results prove that the novel application of ANFIS gives better performance for solving the NILM problem as compared to the other existing techniques.
Highlights
Effective and efficient use of energy is key to sustainable industrial and economic growth in modern times
Two major approaches used for Appliance load monitoring (ALM) are intrusive load monitoring (ILM) and non-intrusive load monitoring (NILM)
The precision results for case 1 show about the actual activation are predicted as activation for respected appliances by the developed adaptive-neuro fuzzy inference system (ANFIS) model whereas the recall shows the results of any appliance activation detection by developed ANFIS model. 0.90 precision rate for toaster shows that from every 100 actual appliance activation 90 (90%) are detected correctly with the developed model
Summary
Effective and efficient use of energy is key to sustainable industrial and economic growth in modern times. Demand-side management (DSM) is a relatively new concept for ensuring efficient energy use at the consumer level It involves the active participation of consumers in load management through different incentives. Load measurements in the system help to determine the appliance operating status and energy consumption of each appliance. ALM involves the study of the appliance usage behavior for the conservation of energy by providing detailed feedback on appliance power consumption to c onsumers[1]. Of each appliance’s power demand whereas, the indirect method involves the installation of sensors with appliances that measure the non-electrical characteristics which are transformed into electrical characteristics later in Ref.[2]. Regular maintenance of these sensors is required to ensure their accurate working These considerations make ILM a secondary technique for load monitoring.
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