SEGSys: A mapping system for segmentation analysis in energy

Customer segmentation analysis can give valuable insights into the energy efficiency of residential buildings. This paper presents a mapping system, SEGSys that enables segmentation analysis at the individual and the neighborhood levels. SEGSys supports the online and offline classification of customers based on their daily consumption patterns and consumption intensity. It also supports the segmentation analysis according to the social characteristics of customers of individual households or neighborhoods, as well as spatial geometries. SEGSys uses a three-layer architecture to model the segmentation system, including the data layer, the service layer, and the presentation layer. The data layer models data into a star schema within a data warehouse, the service layer provides data service through a RESTful interface, and the presentation layer interacts with users through a visual map. This paper showcases the system on the segmentation analysis using an electricity consumption data set and validates the effectiveness of the system

Classification of AMI residential load profiles in the presence of missing data

Domestic energy usage patterns can be reduced to a series of classifications for power system analysis or operational purposes, generalizing household behavior into particular load profiles without noise induced variability. However, with AMI data transmissions over wireless networks becoming more commonplace data losses can inhibit classification negating the benefits to the operation of the power system as a whole. Here, an approach allowing incomplete load profiles to be classified while maintaining less than a 10% classification error with up to 20% of the data missing is presented

Household Power Consumption Forecasting using IoT Smart Home Data

The use of the forecasting system is becoming more prominent in recent years. One of the implementations of the forecasting system is to predict electricity consumption demand. In this paper, we have developed a forecasting system for household electricity consumption using a well-known Extreme Gradient Boosting algorithm. We utilized time-series data from a smart meter dataset to make a predictive model. First, we evaluated the importance of time-series feature from the dataset and resampled the original dataset. Then, we used the resampled data to train the model and calculated training loss function. Our experimental studies with real IoT Smart Home data demonstrate that our forecasting system works well with small dataset using one-hour downsampling on the dataset

Energy Meter Data Analysis Using Machine Learning Techniques

With the advancement of technology, existence of energy meters are not merely to measure energy units. The proliferation of energy meter deployments had led to significant interest in analyzing the energy usage by the machines. Energy meter data is often difficult to analyzeowing to the aggregation of many disparate and complex loads. At utility scales, analysis is further complicated by the vast quantity of data and hence industries turn towards applying machine learning techniques for monitoring and measuring loads of the machines. The energy meter data analysis aims at analyzing the behavior of the machine and providing insights on usage of the energy. This will help the industries to identify the faults in the machine and to rectify it.Two use cases with two different motor specifications is considered for evaluation and the efficiency is proved by considering accuracy, precision, F-measure and recall as metrics