Saima Aman's research while affiliated with University of Southern California and other places

Publications (20)

Conference Paper
Full-text available
As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction...
Conference Paper
The advent of smart meters and advanced communication infrastructures catalyzes numerous smart grid applications such as dynamic demand response, and paves the way to solve challenging research problems in sustainable energy consumption. The space of solution possibilities are restricted primarily by the huge amount of generated data requiring cons...
Conference Paper
Full-text available
Demand response (DR) is a technique used in smart grids to shape customer load during peak hours. Automated DR offers utilities a fine grained control and a high degree of confidence in the outcome. However the impact on the customer's comfort means this technique is more suited for industrial and commercial settings than for residential homes. In...
Conference Paper
Full-text available
The use of AMI in Smart Grids has resulted in huge volumes of energy consumption data being collected. We design a provably efficient online clustering technique based on algorithmic theory to analyze high volume, high dimensional energy consumption data at scale, and on the fly. Unlike prior work, we study the consumption properties of the whole p...
Article
Applications in sustainability domains such as in energy, transportation, and natural resource and environment monitoring, increasingly use sensors for collecting data and sending it back to centrally located processing nodes. While data can usually be collected by the sensors at a very high speed, in many cases, it can not be sent back to central...
Article
Big data applications such as in smart electric grids, transportation, and remote environment monitoring involve geographically dispersed sensors that periodically send back information to central nodes. In many cases, data from sensors is not available at central nodes at a frequency that is required for real-time modeling and decision-making. Thi...
Conference Paper
Full-text available
Growing demand is straining our existing electricity generation facilities and requires active participation of the utility and the consumers to achieve energy sustainability. One of the most effective and widely used ways to achieve this goal in the smart grid is demand response (DR), whereby consumers reduce their electricity consumption in respo...
Article
Full-text available
The performance of prediction models is often based on "abstract metrics" that estimate the model's ability to limit residual errors between the observed and predicted values. However, meaningful evaluation and selection of prediction models for end-user domains requires holistic and application-sensitive performance measures. Inspired by energy co...
Technical Report
Full-text available
Recent years have seen an increasing interest in providing accurate prediction models for electrical energy consumption. In Smart Grids, energy consumption optimization is critical to enhance power grid reliability, and avoid supply-demand mismatches. Utilities rely on real-time power consumption data from individual customers in their service area...
Article
Full-text available
This article focuses on a scalable software platform for the Smart Grid cyber-physical system using cloud technologies. Dynamic Demand Response (D2R) is a challenge-application to perform intelligent demand-side management and relieve peak load in Smart Power Grids. The platform offers an adaptive information integration pipeline for ingesting dyna...
Article
The electric grid is radically evolving and transforming into the smart grid, which is characterized by improved energy efficiency and manageability of available resources. Energy management (EM) systems, often integrated with home automation systems, play an important role in the control of home energy consumption and enable increased consumer par...
Conference Paper
The rising global demand for energy is best addressed by adopting and promoting sustainable methods of power consumption. We employ an informatics approach towards forecasting the energy consumption patterns in a university campus micro-grid which can be used for energy use planning and conservation. We use novel indirect indicators of energy that...
Article
We describe and demonstrate a prototype software architecture to support data-driven demand response optimization (DR) in the USC campus microgrid, as part of the Los Angeles Smart Grid Demonstration Project. The architecture includes a semantic information repository that integrates diverse data sources to support DR, demand forecasting using scal...
Article
Power utilities are increasingly rolling out "smart" grids with the ability to track consumer power usage in near real-time using smart meters that enable bi-directional communication. However, the true value of smart grids is unlocked only when the veritable explosion of data that will become available is in-gested, processed, analyzed and transla...
Article
The advent and growth of smart energy grids is increasing the ability to monitor and communicate power supply, pricing, and demand among utility providers and consumers. While the smart meter infrastructure is expanding at a rapid rate to enable communication using emerging standards, the software architecture to collect, manage, analyze, scale, an...

Citations

... Depending on its size missing data can negatively impact the data-driven methods used in its analysis. Causality has been proposed [1] to improve prediction in case of partial data. While promising, the method works best in case of microgrids and closed environments where dependencies between customers are natural and can be easily determined through data mining. ...
... Generally, the previous research efforts in this domain may be divided into two categories: end-user models and econometric methods. End-user models are commonly used as an alternative to black-box methods (Wood and Newborough, 2003, Abreu and Pereira, 2012, Aman et al., 2011, Kolter and Ferreira Jr, 2011, Beckel et al., 2012). They require information about housing conditions, electrical appliance usage and environmental factors. ...
... These collections are characterized by a diversified structure of high complexity. The main difficulties are data storage, real-time analysis, and data visualization and analysis results [141,142]. The process of examining massive amounts of data to reveal hidden patterns and secret correlations is called Big Data analysis. ...
... Various factors including renewable power generation, power cost in energy markets, and day-ahead planning of load distribution should be predicted. Such factors are of importance in SG's security and sustainability [38,39]. ...
... C4. Load segregation Non-intrusive appliance load monitoring [35], [66], [92], [130], [147], [207], [219], disaggregate smart home sensor data [135], [144], [260], [267] C5. Power loads / consumption analysis consumption clustering ( [109], [136], [137], [182], [190], [198], [199], [206], [210], [221], [259], [263]), consumption prediction ( [?], [6], [10]- [12], [19], [23], [24], [32], [36], [42], [45], [53], [57], [65], [69], [70], [78], [79], [86], [88], [90], [98], [101]- [103], [106], [110], [111], [115]- [117], [122], [124], [125], [132], [146], [152], [156]- [159], [161], [162], [164]- [166], [171], [172], [174], [175], [178], [187], [193], [194], [203], [211], [213], [218], [220], [226]- [228], [237], [240], [242], [253], [255], [264]), consumption data analysis and modelling ( [14], [20], [25], [30], [43], [51], [80], [118], [201], [256]) ...
... Scalable solutions will be required to enable fast and reliable control [1] during demand response (DR) [6] events. The scale of the required interconnections raises many challenges in terms of privacy and security [2], automated control strategies [3], and software solutions for efficient near real-time responsiveness [4] to predictable and unpredictable events. The first steps are already being taken in the form of smart devices, and monitoring and energy management systems (cf. ...
... As discussed earlier, the effective practical implementation of a DR scheme requires the generation of both an accurate and reliable predicted baseline and an accurate and reliable predicted reduced consumption [25]. As shown in Figure 3, the baseline represents a counterfactual prediction (either forecast or backcast, generated on-line or off-line) of electricity consumption for targeted assets during the time-period corresponding to a DR event, under the assumption that no corrective DR action will be/had been taken. ...
... Modeli përdor të dhëna të matura në një periudhë 3 vjeçare të siguruara nga matësit dixhital të instaluar në ndërtesë, dhe performanca e modelit përmirësohet krahasuar modelet bazë me 53%. Po kështu e njëjta autore në [125] përdor këto modele për të analizuar më tej natyrën e konsumit si dhe faktorët që ndikojnë në të për një "smart-grid". Po ashtu studimi i parashikimit të konsumit në rrjetat smart bëhet në [127], ku parashikimi i konsumit në ndërtesa tipike të mëdha me anë të modelit të pemës (mbCRT) tregon përmirësime të mëdha kundrejt modeleve të tjera duke sjellë reduktim të konsumit dhe kursime vjetore deri 45000 $ për një ndërtesë komerciale. ...
... Various factors including renewable power generation, power cost in energy markets, and day-ahead planning of load distribution should be predicted. Such factors are of importance in SG's security and sustainability [38,39]. ...
... In current years, researchers have coined the novel term "Industry 4.0" [17]. The ter refers to the fourth industrial revolution. ...