Abstract
With the rapid development of IoT based home appliances, it has become a possibility that home owners share with Utilities in the management of home appliances energy consumption. Thus, the proposed work empowers home owners to manage their home appliances energy consumption and allow them to compare their consumption with respect to their local community total consumption. This serves as a nudge in consumer’s behavior to schedule their home appliances operation according to their local community consumption profile and trend. Utilizing the same common communication infrastructure, it also allows the utilities on different consumption levels (community, state, country) to monitor and visualize the energy consumption in their respective grid segments on daily, monthly, and yearly basis. A high-speed distributed computing cluster based on commodity hardware with efficient big data mathematical algorithm is employed in this work. To achieve this, two big data processing paradigms are evaluated with a set of qualitative and quantitative metrics with subsequent recommendations. One million smart meter data is simulated to access individual homes. With the utilization of distributed storage and computing cluster for handling energy big data, the utilities can perform consumer load analysis and visualization on a scale of one million consumers. This helps the utilities in providing consumers a more accurate representation of how much energy they are consuming with greater granularity and with respect to their local community. Consumer and Utility centric queries are developed to create a web-based real time energy consumption management system presented in terms of dashboard charts, graphs, and reports that can be accessed by the consumer and utility providers remotely.
Highlights
The smart meters are playing a major role in the growing energy management system
In our experiments as shown in Table-2, auto-regressive moving average (ARIMA) (2, 1, 3) is considered the best model to make energy consumption forecasts for house appliances as it yields the lowest values for Akaike Information Criterion (AIC), Akaike Information Criterion Corrected (AICc) and Bayesian Information Criterion (BIC)
Two experiments are designed to study the effect on output variables of two big data querying engines characterized by in-memory and disk caching. (A) Experimental Objective I: To determine the total latency for data querying, each query was scheduled to run 100 times using a scheduled workflow tool [25] on top of the distributed file system of the cluster. (B) Experimental Objective II: To determine the processor throughput, data querying throughput was based on the file size and the elapsed time to do so
Summary
The smart meters are playing a major role in the growing energy management system. IoT based smart meters read energy consumption from residential areas home appliances generating data that typically exhibits the 3V characteristics of big data; Volume, Variety, and Velocity [1]. Scaling up to a 4-nodes distributed file system storage and processing cluster [15] using the commodity hardware, a performance analysis experiment is conducted utilizing two open source big data processing engines for achieving an optimized querying, visualization platform for efficient monitoring and managing energy use. With the help of distributed file storage and computing cluster, the utilities can conduct consumer load profiling and load analysis on a large scale of 1 million customers which helps in providing the end user/consumer a more accurate representation of how much energy they are consuming with greater granularity and with respect to their community neighborhoods as well This facilitates real time monetization to all the one million home owners with minimal response time and high aggregated network throughput. Summary and future work is presented in the conclusion section 9
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