Estimating Power Consumption of Airconditioners Using a Sequencetosequence Model We suggest a neural network based power estimation algorithm for airconditioners exploiting a sequencetosequence model which is widely used in the field of natural language processing. Our proposed method exploits both past usage patterns and weather forecasts for better prediction because airconditioners have high dependency on the environmental factors. Our proposed algorithm is trained and validated on realworld data collected in various regions of the Republic of Korea. For validation, we adopt two evaluation metrics, mean absolute error and mean absolute percentage error. We accordingly suggest two different types of accuracy metrics practical for end users. Applying the proposed method to realworld data from airconditioners, we have 5.7 kW of mean absolute error and 23 percent of mean absolute percentage error. Accuracy out of each metric is 85.02 percent and 74.65 percent respectively which outperforms the conventional regression algorithms.

A NonIntrusive Appliance Recognition System Depleting energy resources and the unstable supply of raw materials call for innovations in the energy industry, such as in energy generation, distribution, and management. Moreover, increasing electricity prices take a toll on consumers that operates within a strict budget. This study in particular focuses on the proper management and utilization of energy from the consumeru0027s perspective. The objective is to develop a nonintrusive appliance recognition system that can identify the appliances that are being used and calculate how much each of these appliances contribute to the total electricity consumption. Installation of the monitoring system, which utilizes a single sensor clamped to the main power line and used to measure the total energy consumption, does not alter the electrical system, thus, nonintrusive. With this system, the homeowner can monitor which appliances are in use and how much energy they consume. Also, this translates to savings for the household when data provided by the system lead to smarter energymanagement choices. For this study to be deployable in households, a dataacquisition system to streamline the datagathering procedure was needed. Also, a machine learning algorithm was trained and implemented to perform the appliance recognition task given input features from the frequency domain of the measured aggregate data from the main power line. Lastly, the system was tested for prediction accuracy and characterized; and then necessary optimizations were implemented.