@unpublished{yadav2024fine,
  title = {Fine-tuning Large Language Models for Automated Diagnostic Screening Summaries},
  author = {Yadav, Manjeet and Sahu, Nilesh Kumar and Chaturvedi, Mudita and Gupta, Snehil and Lone, Haroon R},
  journal = {arXiv preprint arXiv:2403.20145},
  year = {2024}
}

@unpublished{jaiswal2024harnessing,
  title = {Harnessing Smartwatch Microphone Sensors for Cough Detection and Classification},
  author = {Jaiswal, Pranay and Lone, Haroon R.},
  year = {2024},
  eprint = {2401.17738},
  archiveprefix = {arXiv},
  primaryclass = {cs.SD}
}

@unpublished{toner2023wearable,
  title = {Wearable sensor-based multimodal physiological responses of socially anxious individuals across social contexts},
  author = {Toner, Emma R and Rucker, Mark and Wang, Zhiyuan and Larrazabal, Maria A and Cai, Lihua and Datta, Debajyoti and Thompson, Elizabeth and Lone, Haroon and Boukhechba, Mehdi and Teachman, Bethany A and others},
  journal = {arXiv preprint arXiv:2304.01293},
  year = {2023}
}

@article{uikey2024Indian,
  title = {Indian Traffic Sign Detection and Classification Through a Unified Framework},
  author = {Uikey, Rishabh and Lone, Haroon R and Agarwal, Akshay},
  journal = {IEEE transactions on intelligent transportation systems},
  volume = {},
  number = {},
  pages = {},
  year = {2024},
  publisher = {IEEE},
  doi = {10.1109/TITS.2024.3411117}
}

Traffic sign boards are vital in facilitating smart transportation systems. More than 90% of accidents happen due to drivers’ inattentiveness over these boards. Hence, relaying traffic sign board information automatically to drivers becomes crucial to avoid such accidents. While numerous traffic sign detection and classification systems exist, it is important to note that these automated systems have not been adequately assessed within the context of Indian settings. The task of traffic sign detection and classification presents unique challenges in the Indian context due to the presence of multiple variations for a single action. This paper proposes a robust methodology for detecting and classifying traffic signboards simultaneously using deep learning.  The method uses convolutional models like AlexNet, VGG-19, ResNet-50, and EfficientNet v2 as the network backbone with different loss functions for bounding box regression and classification. Along with the proposed method, we collected the Indian traffic signs and information boards dataset. The collected dataset consists of 4300+ raw images without any augmentation. We evaluated our method on the collected dataset and found the detection and classification accuracies as 85.5%  and 98.5%, respectively. With this paper, we release the dataset publicly.

@article{sahu2024wearable,
  title = {Wearable Technology Insights: Unveiling Physiological Responses During Three Different Socially Anxious Activities},
  author = {Sahu, Nilesh Kumar and Gupta, Snehil and Lone, Haroon R},
  journal = {ACM Journal on Computing and Sustainable Societies},
  year = {2024},
  publisher = {ACM New York, NY},
  doi = {10.1145/3663671}
}

Wearable technology holds promise for monitoring and managing Social Anxiety Disorder (SAD), yet the absence of clear biomarkers specific to SAD hampers its effectiveness. This paper explores this issue by presenting a study investigating variances in heart rate, heart rate variability, and skin conductance between socially anxious and non-anxious individuals. One hundred eleven non-clinical student participants participated in groups of three in three anxiety-provoking activities (i.e., speech, group discussion, and interview) in a controlled lab-based study. During the study, electrocardiogram (ECG) and electrodermal activity (EDA) signals were captured via on-body electrodes. During data analysis, participants were divided into four groups based on their self-reported anxiety level (“None”, “mild”, “moderate”, and “severe”). Between-group analysis shows that 
 discriminating ECG features (i.e., HR and MeanNN) could identify anxious individuals during anxiety-provoking activities, while EDA could not. 
Moreover, the discriminating ECG features improved the classification accuracy of anxious and non-anxious individuals in different machine-learning techniques.  The findings need to be further scrutinized in real-world settings for the generalizability of the results.

@article{sahu2024unveiling,
  title = {Unveiling Social Anxiety: Analyzing Acoustic and Linguistic Traits in Impromptu Speech within a Controlled Study},
  journal = {ACM Journal on Computing and Sustainable Societies},
  author = {Sahu, Nilesh Kumar and Yadav, Manjeet and Lone, Haroon R},
  year = {2024},
  publisher = {ACM New York, NY},
  doi = {10.1145/3657245},
  url = {https://dl.acm.org/doi/10.1145/3657245},
  keywords = {Digital health, social anxiety disorder, acoustic feature, linguistic feature, digital biomarkers}
}

Early detection and treatment of Social Anxiety Disorder (SAD) is crucial. However, current diagnostic methods have several drawbacks, including being time-consuming for clinical interviews, susceptible to emotional bias for self-reports, and inconclusive for physiological measures. Our research focuses on a digital approach using acoustic and linguistic features extracted from participants’ “speech” for diagnosing SAD. Our methodology involves identifying correlations between extracted features and SAD severity, selecting the effective features, and comparing classical machine learning and deep learning methods for predicting SAD. Our results demonstrate that both acoustic and linguistic features outperform deep learning approaches when considered individually. Logistic Regression proves effective for acoustic features, while Random Forest excels with linguistic features, achieving the highest accuracy of 85.71%. Our findings pave the way for non-intrusive SAD diagnosing that can be used conveniently anywhere, facilitating early detection.

@article{MISHRA2024113950,
  title = {DECODE: Data-driven Energy Consumption Prediction leveraging Historical Data and Environmental Factors in Buildings},
  journal = {Energy and Buildings},
  pages = {113950},
  year = {2024},
  issn = {0378-7788},
  doi = {10.1016/j.enbuild.2024.113950},
  url = {https://www.sciencedirect.com/science/article/pii/S0378778824000665},
  author = {Mishra, Aditya and Lone, Haroon R. and Mishra, Aayush},
  keywords = {Energy Forecasting, Machine Learning, Deep Learning, Long Short-Term Memory (LSTM)}
}

Energy prediction in buildings plays a crucial role in effective energy management. Precise predictions are essential for achieving optimal energy consumption and distribution within the grid. This paper introduces a Long Short-Term Memory (LSTM) model designed to forecast building energy consumption using historical energy data, occupancy patterns, and weather conditions. The LSTM model provides accurate short, medium, and long-term energy predictions for residential and commercial buildings compared to existing prediction models. We compare our LSTM model with established prediction methods, including linear regression, decision trees, and random forest. Encouragingly, the proposed LSTM model emerges as the superior performer across all metrics. It demonstrates exceptional prediction accuracy, boasting the highest R2 score of 0.97 and the most favorable mean absolute error (MAE) of 0.007. An additional advantage of our developed model is its capacity to achieve efficient energy consumption forecasts even when trained on a limited dataset. We address concerns about overfitting (variance) and underfitting (bias) through rigorous training and evaluation on real-world data. In summary, our research contributes to energy prediction by offering a robust LSTM model that outperforms alternative methods and operates with remarkable efficiency, generalizability, and reliability.

@article{jaiswal2023comparative,
  title = {Comparative Assessment of Smartwatch Photoplethysmography Accuracy},
  author = {Jaiswal, Pranay and Sahu, Nilesh Kumar and Lone, Haroon R},
  journal = {IEEE Sensors Letters},
  year = {2023},
  publisher = {IEEE},
  doi = {10.1109/LSENS.2023.3342292}
}

Nowadays, photoplethysmography (PPG) sensor integrated into smartwatches monitors heart health. Despite its convenience, PPG is susceptible to motion artifacts and noise. In this work, we rigorously investigate the smartwatch’s PPG accuracy against medical-grade electrocardiogram (ECG) and PPG devices during different activities (i.e., sitting, standing, and walking). We conducted a user study with 32 healthy student participants and computed different heart rate (HR) features from the devices. Our analysis shows that the smartwatch PPG features, pnn50, low-frequency power (LF), and high-frequency power (HF), differ significantly from the ECG device during the activities. However, other time-domain features, such as HR, mean normal, standard deviation of normal, and root mean square of successive differences do not differ significantly during different activities. Furthermore, both the medical-grade and smartwatch PPG measurements (pnn50, HF, and LF) differed significantly from the ECG during standing and walking. The smartwatch’s PPG accuracy compared with the ECG device reinforces its potential as an alternative health monitoring device.

@article{rashid2020predicting,
  title = {Predicting subjective measures of social anxiety from sparsely collected mobile sensor data},
  author = {Rashid, Haroon and Mendu, Sanjana and Daniel, Katharine E and Beltzer, Miranda L and Teachman, Bethany A and Boukhechba, Mehdi and Barnes, Laura E},
  journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
  volume = {4},
  number = {3},
  pages = {1--24},
  year = {2020},
  publisher = {ACM New York, NY, USA},
  doi = {10.1145/3411823}
}

Exploiting the capabilities of smartphones for monitoring social anxiety shows promise for advancing our ability to both identify indicators of and treat social anxiety in natural settings. Smart devices allow researchers to collect passive data unobtrusively through built-in sensors and active data using subjective, self-report measures with Ecological Momentary Assessment (EMA) studies. Prior work has established the potential to predict subjective measures from passive data. However, the majority of the past work on social anxiety has focused on a limited subset of self-reported measures. Furthermore, the data collected in real-world studies often results in numerous missing values in one or more data streams, which ultimately reduces the usable data for analysis and limits the potential of machine learning algorithms. We explore several approaches for addressing these problems in a smartphone based monitoring and intervention study of eighty socially anxious participants over a five week period. Our work complements and extends prior work in two directions: (i) we show the predictability of seven different self-reported dimensions of social anxiety, and (ii) we explore four imputation methods to handle missing data and evaluate their effectiveness in the prediction of subjective measures from the passive data. Our evaluation shows imputation of missing data reduces prediction error by as much as 22%. We discuss the implications of these results for future research.

@article{rashid2019blend,
  title = {I-BLEND, a campus-scale commercial and residential buildings electrical energy dataset},
  author = {Rashid, Haroon and Singh, Pushpendra and Singh, Amarjeet},
  journal = {Scientific data, Nature},
  volume = {6},
  number = {1},
  pages = {1--12},
  year = {2019},
  publisher = {Nature Publishing Group},
  doi = {10.1038/sdata.2019.15}
}

Efficient energy consumption at the building level is vital for sustainability. Providing energy efficient systems and solutions requires an understanding of how energy gets consumed. However, there is a general lack of large-scale open datasets about the energy consumption of buildings, which hinders the research. The recent emergence of smart energy meters makes it possible to collect such data, which can then be used for analysis. In this paper, we release I-BLEND, 52 months of electrical energy dataset at a one-minute sampling rate from commercial and residential buildings of an academic institute campus in an emerging economy, India. Also, we provide occupancy datasets at a 10-minute sampling rate for each of the campus buildings. To the best of our knowledge, this is the first such dataset from India. Public availability of such fine-granular data will allow users to perform different research tasks such as analyzing the impact of weather or occupancy schedule on energy consumption, detecting anomalies, and developing algorithms for predictive maintenance.

@article{rashid2019can,
  title = {Can non-intrusive load monitoring be used for identifying an appliance’s anomalous behaviour?},
  author = {Rashid, Haroon and Singh, Pushpendra and Stankovic, Vladimir and Stankovic, Lina},
  journal = {Applied energy},
  volume = {238},
  pages = {796--805},
  year = {2019},
  publisher = {Elsevier},
  doi = {10.1016/j.apenergy.2019.01.061}
}

Identification of faulty appliance behaviour in real time can signal energy wastage and the need for appliance servicing or replacement leading to energy savings. The problem of appliance fault or anomaly detection has been tackled vastly in relation to submetering, which is not scalable since it requires separate meters for each appliance. At the same time, for applications such as energy feedback, Non-intrusive load monitoring (NILM) has been recognised as a scalable and practical alternative to submetering. However, the usability of NILM for anomaly detection has not yet been investigated. Since the goal of NILM is to provide energy consumption estimate, it is unclear if the signal fidelity of appliance signatures generated by state-of-the-art NILM is sufficient to enable accurate appliance fault detection. In this paper, we attempt to determine whether appliance signatures detected by NILM can be used directly for anomaly detection. This is carried out by proposing an anomaly detection algorithm which performs well for submetering data and evaluate its ability to identify the same faulty behaviour of appliances but with NILM-generated appliance power traces. Our results on a dataset of six residential homes using four state-of-the-art NILM algorithms show that, on average, NILM traces are not as robust to identification of faulty behaviour as compared to using submetered data. We discuss in detail observations pertaining to the reconstructed appliance signatures following NILM and their fidelity with respect to noise-free submetered data.

@article{rashid2015dead,
  title = {Dead reckoning localisation technique for mobile wireless sensor networks},
  author = {Rashid, Haroon and Turuk, Ashok Kumar},
  journal = {IET Wireless Sensor Systems},
  volume = {5},
  number = {2},
  pages = {87--96},
  year = {2015},
  publisher = {Wiley Online Library},
  doi = {10.1049/iet-wss.2014.0043}
}

Localisation in wireless sensor networks (WSNs) not only provides a node with its geographical location but also a basic requirement for other applications such as geographical routing. Although a rich literature is available for localisation in static WSN, not enough work is done for mobile WSNs, owing to the complexity because of node mobility. Most of the existing techniques for localisation in mobile WSNs use Monte Carlo localisation (MCL), which is not only time consuming but also memory intensive. They, consider either the unknown nodes or anchor nodes to be static. In this study, the authors propose a technique called dead reckoning localisation for mobile WSNs (DRLMSN). In the proposed technique all nodes (unknown nodes as well as anchor nodes) are mobile. Localisation in DRLMSN is done at discrete time intervals called checkpoints. Unknown nodes are localised for the first time using three anchor nodes. For their subsequent localisations, only two anchor nodes are used. The proposed technique estimates two possible locations of a node using Bézout’s theorem. A dead reckoning approach is used to select one of the two estimated locations. The authors have evaluated DRLMSN through simulation using Castalia simulator, and is compared with a similar technique called received signal strength-MCL.

@article{rashid2013localization,
  title = {Localization of wireless sensor networks using a single anchor node},
  author = {Rashid, Haroon and Turuk, Ashok Kumar},
  journal = {Wireless personal communications},
  volume = {72},
  pages = {975--986},
  year = {2013},
  publisher = {Springer},
  doi = {10.1007/s11277-013-1050-y}
}

Localization of nodes in a sensor network is essential for the following two reasons: (i) to know the location of a node reporting the occurrence of an event, and (ii) to initiate a prompt action whenever necessary. Different localization techniques have been proposed in the literature. Most of these techniques use three location aware nodes for localization of an unknown node. Moreover, the localization techniques also differ from environment to environment. In this paper, we proposed a localization technique for grid environment. Sensor nodes are deployed in a grid pattern and localization is achieved using a single location aware or anchor node. We have identified three types of node in the proposed scheme: (i) Anchor node, (ii) Unknown node and (iii) Special node. First, the special nodes are localized with respect to the anchor node, then the unknown nodes are localized using trilateration mechanism. We have compared the proposed scheme with an existing localization algorithm for grid deployment called Multiduolateration. The parameters considered for localization are localization time and localization error. It is observed that localization time and error in the proposed scheme is lower than that of Multiduolateration.

@inproceedings{sahoo2024mapping,
  title = {Mapping Thermal Footprints: Occupancy Estimation and Localization in Diverse Indoor Settings with Thermal Arrays},
  author = {Sahoo, Soumya R and Lone, Haroon R},
  booktitle = {Proceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)},
  pages = {},
  year = {2024},
  doi = {10.1145/3674829.3675059}
}

Estimating and locating occupants indoors is crucial for automating operations within buildings. However, current privacy-preserving occupancy estimation systems using thermal cameras have not been thoroughly evaluated in dense settings, such as classrooms or movie theaters, where occupants sit close to one another. Estimating occupancy in such settings presents challenges, as nearby occupants often lead to clusters of thermal signatures, thereby affecting the accuracy of the estimation. In response to these challenges, our work proposes Machine Learning (ML) and Deep Learning (DL) based methods for occupancy estimation and localization, both in dense and sparse settings. The ML method complements existing occupancy estimation techniques by incorporating new manual features, while the DL method performs automatic feature extraction, accurate occupancy estimation and localization. Remarkably, the results demonstrate a significant enhancement in occupancy estimation accuracy of up to 10%, achieving an impressive overall accuracy rate of 97%. Moreover, our evaluation on an edge device confirms the practicality and relevance of the proposed methods in real-world applications.

@inproceedings{mishra2024deep,
  title = {Towards Safer Roads: Deep Learning for Rash Driving Detection using Smartphone Sensors Data},
  author = {Mishra, Durgesh and Gulati, Manoj and Lone, Haroon R},
  booktitle = {Proceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)},
  pages = {},
  year = {2024},
  doi = {10.1145/3674829.3675069}
}

Rash driving detection is vital to prevent accidents and improve public safety. Existing rash driving solutions using hand-crafted features have several limitations. We propose a simple yet efficient two-step process to overcome the limitations of the existing works by leveraging smartphone sensor (accelerometer and gyroscope) data. The first step filters out normal driving data and retains only the abnormal driving data with the proposed Adaptive Time Window (ATW) algorithm. This not only enhances the accuracy of detection but also reduces computation time, making our solution more efficient. Importantly, the proposed ATW algorithm completely eliminates window overlap redundancy and edge effects in the system. The second step classifies abnormal driving patterns with the proposed 1D CNN model. Our results demonstrate that the proposed solution is highly accurate and has a weighted accuracy of 97.14%. Additionally, as part of this research, we have curated and released a labeled Indian dataset comprising five distinct rash driving patterns: Lane Weaving, Lane Swerving, Hard Braking, Hard Cornering, and Quick U-turn. This dataset can be valuable for further studies and aid in developing multimodal rash driving detection systems.

@inproceedings{harshit2024eyes,
  title = {Eyes Speak Louder: Harnessing Deep Features From
  Low-Cost Camera Video for Anxiety Detection},
  author = {Harshit, Nandigramam Sai and Sahu, Nilesh Kumar and Lone, Haroon R},
  booktitle = {Proceedings of the 10th Workshop on Body-Centric Computing Systems, colocated with ACM MobiSys 2024},
  pages = {},
  year = {2024},
  publisher = {},
  doi = {10.1145/3662009.3662021}
}

Social anxiety is a common mental health disorder, affecting approximately 36% of the world’s population. Currently, diagnosing social anxiety disorder relies on clinical interviews
and self-reported questionnaires, which are prone to subjective biases. Discovering non-intrusive, objective markers of anxiety, such as eye gaze patterns, could enhance current diagnostic approaches and facilitate early assessment, thereby reducing the treatment gap. Previous research has explored
predicting anxiety states using physiological signals and audio data, but conclusive results remain elusive. In this study, we propose leveraging eye gaze features to
predict one’s anxiety levels using deep eye gaze features. One hundred eleven participants engaged in an anxiety provoking speech activity were recorded using a low-cost smartphone camera. Further, we used a variational autoencoder to extract deep eye gaze features from the raw video features, which were then fed into machine-learning models to predict participant’s anxiety levels. Our result shows that
the Random Forest classification model achieved the highest average accuracy, precision, recall, & F1 score of 100% (averaged over five folds), suggesting its capability to detect
participant’s anxiety in real-world settings.

@inproceedings{sahoo2023occupancy,
  title = {Occupancy counting in dense and sparse settings with a low-cost thermal camera},
  author = {Sahoo, Soumya R and Lone, Haroon R},
  booktitle = {2023 15th International Conference on COMmunication Systems \& NETworkS (COMSNETS)},
  pages = {537--544},
  year = {2023},
  organization = {IEEE},
  doi = {10.1109/COMSNETS56262.2023.10041292}
}

Occupancy detection and counting are crucial to automate buildings’ lighting, heating, and cooling systems. However, most existing occupancy detection and counting techniques are underperforming or privacy-averse. In this work, we propose an adaptive blob filtering algorithm for processing thermal images and evaluate several classification algorithms for occupancy counting. We evaluate the performance of the proposed algorithm using a low-cost privacy-preserving thermal camera under both sparse and dense occupancy settings. Our results show that the proposed algorithm improves occupancy counting results significantly compared to two existing baselines, resulting in an average accuracy of 84.5% under dense classroom settings.

@inproceedings{rashid2019evaluation,
  title = {Evaluation of non-intrusive load monitoring algorithms for appliance-level anomaly detection},
  author = {Rashid, Haroon and Stankovic, Vladimir and Stankovic, Lina and Singh, Pushpendra},
  booktitle = {ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages = {8325--8329},
  year = {2019},
  organization = {IEEE},
  doi = {10.1109/ICASSP.2019.8683792}
}

Appliance fault in buildings resulting in abnormal energy consumption is known as an anomaly. Traditionally, anomaly detection is performed either at aggregate, i.e., meter-level, or at appliance level. Meter-level anomaly detection does not identify the anomaly-causing appliance, while appliance-level detection requires submetering each appliance in the building. Non-Intrusive Load Monitoring (NILM) has been proposed as an alternative to submetering to detect when appliances are running as well as estimate the appliance energy consumption. So far, applications have revolved around meaningful energy feedback. In this paper, we assess whether NILM can indeed be used for anomaly detection, as an alternative to submetering. We propose a supervised anomaly detection approach, AEM, and evaluate the effectiveness of NILM for anomaly detection. The proposed approach first learns an appliance’s normal operation and then monitors its energy consumption for anomaly detection. We resort to real data, aggregate and subme-tered data from the two-year long REFIT dataset. We explain why anomaly detection performs worse with NILM data as compared to submetered data, highlighting the need for new, anomaly-aware NILM approaches.

@inproceedings{rashid2018rimor,
  title = {Rimor: Towards identifying anomalous appliances in buildings},
  author = {Rashid, Haroon and Batra, Nipun and Singh, Pushpendra},
  booktitle = {Proceedings of the 5th Conference on Systems for Built Environments},
  pages = {33--42},
  year = {2018},
  doi = {10.1145/3276774.3276797}
}

Buildings across the world contribute about one-third of the total energy consumption. Studies report that anomalies in energy consumption caused by faults and abnormal appliance usage waste up to 20% of energy in buildings. Recent works leverage smart meter data to find such anomalies; however, such works do not identify the appliance causing the anomaly. Moreover, most of these works are not real-time and report the anomaly at the end of the day. In this paper, we propose a technique named Rimor that addresses these limitations. Rimor predicts the energy consumption of a home using historical energy data and contextual information and flags an anomaly when the actual energy consumption deviates significantly from the predicted consumption. Further, it identifies anomalous appliance(s) by using easy-to-collect appliance power ratings. We evaluated it on four real-world energy datasets containing 51 homes and found it to be 15% more accurate in detecting anomalies as compared to four other baseline approaches. Rimor reports an appliance identification accuracy of 82%. In addition, we also release an anomaly annotated energy dataset for the research community.

@inproceedings{rashid2018monitor,
  title = {Monitor: An abnormality detection approach in buildings energy consumption},
  author = {Rashid, Haroon and Singh, Pushpendra},
  booktitle = {2018 IEEE 4th international conference on collaboration and internet computing (CIC)},
  pages = {16--25},
  year = {2018},
  organization = {IEEE},
  doi = {10.1109/CIC.2018.00-44}
}

With the growth of smart cities, more buildings are now being instrumented with smart meters for providing better energy efficiency for sustainable development. Buildings consume around 39% of electrical energy worldwide and studies report that wasteful consumer behavior such as forgetting to switch off an appliance after use or using an appliance with misconfigured settings adds about one-third to buildings consumption. These instances result in deviations in energy consumption as compared to its normal consumption and are called as abnormalities. Detecting such abnormalities is important for reducing energy wastage. Existing methods detect abnormalities by analyzing smart meter data, however, they result in a high number of false positive alarms. This inaccuracy results in ignoring the alarms by building administrators which also affects genuine alarms. Thus, reducing the false positive alarms and making detection algorithms more accurate is a major aim. In this paper, we present our novel approach, called Monitor, which first identifies patterns in past consumption data and then uses these patterns to detect abnormalities. Our approach requires smart meter data only and reduces the rate of false positive alarms considerably. We have evaluated our approach on 16 weeks smart meter data of real world buildings. The comparison of this approach with existing approaches shows that our approach improves the accuracy by up to 24% in best scenario and on average by 14%. This improvement in accuracy reduces the rate of false positive alarms significantly and makes it more suitable for real-world deployments.

@inproceedings{mammen2018want,
  title = {Want to reduce energy consumption, whom should we call?},
  author = {Mammen, Priyanka Mary and Kumar, Hareesh and Ramamritham, Krithi and Rashid, Haroon},
  booktitle = {Proceedings of the Ninth International Conference on Future Energy Systems},
  pages = {12--20},
  year = {2018},
  doi = {10.1145/3208903.3208941}
}

Power shortage is a serious issue in developing nations. During periods of high demand, utilities need to motivate the consumers to curtail their consumption for maintaining grid stability and avoiding blackouts or brownouts. Identification of suitable candidates is essential for such events, as the budget set aside by utilities for Demand Response (DR) events for providing incentives to the consumers should not exceed the added production cost due to peaks. Similarly, from the consumers’ point of view, participation comes with the compromise to their convenience. Hence, the selection criteria should be such that it minimizes the peaking cost to the utility without affecting consumer comfort. In this paper, we present SmarDeR, a smart DR consumer selection strategy which considers several factors and consolidates them into a single function which can work in different modes to strategically choose the candidates for the DR event based on the goals specified by the utility. We evaluate different policies and metrics for approaching the right consumers for participating in the DR events. Thereby, we can maintain a fair distribution of requests among the most relevant and reliable users. Experiments with smart-meter data from apartments in our campus demonstrates the effectiveness of our SmarDeR approach

@inproceedings{rashid2017want,
  title = {Want to reduce energy consumption? don't depend on the consumers!},
  author = {Rashid, Haroon and Mammen, Priyanka Mary and Singh, Siddharth and Ramamritham, Krithi and Singh, Pushpendra and Shenoy, Prashant},
  booktitle = {Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments},
  pages = {1--4},
  year = {2017},
  doi = {10.1145/3137133.3137164}
}

Motivating users to save energy is considered to be the holy grail of smart energy management. However, many studies have shown that changing user behavior from an energy standpoint is a very difficult problem. Furthermore, in countries such as the United States, users do not have sufficient monetary incentives to become energy conscious, given the low cost of electricity, and more generally, energy. In this paper, we study this issue in a developing economy and present a user study of 41 apartments in a high-rise apartment complex in India. Through a combination of fine-grain energy meter usage data and detailed user surveys, we find that these users may be no more energy conscious or motivated to adopt energy efficiency measures than their counterparts in Western nations. Our study challenges the belief that energy prices are higher in developing regions and hence, users in developing regions tend to be more energy-aware than those elsewhere. Consequently, and importantly, we argue that utility companies, rather than end-users, should be the vanguard for realizing energy efficiency improvement at consumer premises in order to obtain grid-wide benefits such as peak load reduction or avoiding blackouts. Towards this goal, we argue for a sustained research effort into utility-scale energy analytic approaches, for example, to identify end users who are large consumers along with the underlying causes of their consumption. Utilities can deploy such approaches and then aggressively target these users for energy efficiency improvements.

@inproceedings{rashid2017revisiting,
  title = {Revisiting selection of residential consumers for demand response programs},
  author = {Rashid, Haroon and Singh, Pushpendra and Ramamritham, Krithi},
  booktitle = {Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments},
  pages = {1--4},
  year = {2017},
  doi = {10.1145/3137133.3137157}
}

Electrical utilities depend on Demand Response programs to manage peak loads by incentivizing consumers to voluntarily curtail a portion of their load during a specified period. Utilities first categorize consumers based on their energy consumption patterns into different clusters and then request consumers of a particular cluster to participate in the demand response program. At a coarse level, clustering approaches do well, but we may not be able to correctly predict which cluster’s profile will fit that day’s power availability. We address this issue by examining the consistency of consumer’s consumption patterns across several consecutive days. We demonstrate that measuring consistency quantitatively helps to understand predictability of consumer’s energy consumption. In the rest of the paper, we provide details of our proposed consistency metric. Further, we propose a methodology to select a few consumers among the consistent ones such that they have a peak at the time specified by the demand response program. We validate our approach using real-world energy consumption data from residential buildings.