Fig 18 - uploaded by Balaji Kalluri
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Difference in power consumption between an ENERGY-STARlabeled HP laptop and a non-labeled ACER laptop.
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![Fig. 1. Projection of energy demand in 2010 vs. 2035 (source: [7]).](profile/Balaji-Kalluri/publication/303817773/figure/fig14/AS:668456731045901@1536383912527/Projection-of-energy-demand-in-2010-vs-2035-source-7_Q320.jpg)
![Fig. 2. ACme sensor mote device (left) and networking (right) [21]](profile/Balaji-Kalluri/publication/303817773/figure/fig1/AS:370864085323778@1465432296913/ACme-sensor-mote-device-left-and-networking-right-21_Q320.jpg)
The commercial sector is responsible in a largedegree for the overall energy consumption around the world andInformation and Communication Technologies (ICT) constitutean important category of electricity loads which is becomingdominant in offices. Recognizing the importance of using ICTequipment more rationally for saving energy in commercialbuild...
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In the last few decades, vehicles are equipped with a plethora of sensors which can provide useful measurements and diagnostics for both the vehicle’s condition as well as the driver’s behaviour. Furthermore, the rapid increase for transportation needs of people and goods together with the evolution of Information and Communication Technologies (IC...
Citations
... It was found that mon-itoring, analyzing and cutting down energy wastage from such appliances in office buildings are keys to improve building energy efficiency. Relevant studies [7,8] outline some best practices in the rational use of ICT appliances in offices. Some examples of such best practices include switching-off desktop PCs and printers at night, adjusting brightness and effective power management of monitors etc., with an estimate of approximately 35% of potential savings. ...
... desktop PC) play a key role in improving energy conservation in offices. The guide to good practices in operating ICT devices in offices has shown potential energy savings of more than 130,000 kWh annually [7]. The actionable feedback to occupants have shown a potential of 311 kWh of savings from desktop PCs within a small office environment involving 18 occupants [9]. ...
... The problem aims at improving the energy efficiency of office plug load appliances not at the design stage but when buildings are in operation. For instance, operating ICT appliances rationally in offices within university campus has shown potential energy savings of more than 130,000 kWh annually [2]. The knowledge of appropriate appliance operation together with actionable feedback has shown a potential of 311kWh of desktop PC energy savings in a small office [3]. ...
Smart building energy management requires knowledge of individual appliance operation from reduced metering points. The key purpose of this study is to present a classification framework for offices that can help discover individual appliances and its operational modes from single-point aggregate measurements. This approach to non-intrusive load monitoring is supervised through labeled Office Plug Load Dataset. The classification approach is based on short episodes (also called subsequences) from time-series dataset within which appliance events lie hidden. A popular technique for discretizing time-series data known as Symbolic Aggregate approXimation lies at the heart of this framework. Mining large time-series dataset, extracting characteristic appliance features and classifying them appropriately based on individual appliance events is facilitated through “Bag of Patterns” based Vector Space Model. This study focuses on classifying multiple events from three common aggregate appliance use-case scenarios in an office environment. The approach is promising at analyzing subsequence patterns from more than 1700 time-series episodes in the dataset. The results from classifying multi-functional device operations from aggregate signature show errors less than 22% in scenario where three appliances are in operation, whereas error is less than 37% when two appliances are in operation. The results also indicate that the approach is likely to work better as the dataset grows as in the case of big data. Additionally, the proposed approach enables visualizing subsequences of a time-series using color-coding scheme. Such visualization helps in understanding the relative specificity of an event to others in the time series
In response to the sustainable development goals and climate change, this paper presents a case study of an existing institutional building located in composite climate zone at Chandigarh, India, for retrofitting it as a Net Zero Energy Building (NZEB). Its cost benefit analysis and payback period has been carried out. This analysis determines whether the benefits outweigh the cost of implementation of NZEB. The before and after energy demands of the building has been simulated on eQUEST and validated with actual energy bills. The results of energy simulation has been used to arrive at the potential energy savings. A 250 kWp onsite grid interactive solar PV system on rooftops of existing buildings is recommended to meet the energy demand of the building, after implementation of retrofitting measures. Simulation results indicated that annual reduction in energy demand by retrofitting of building envelope components is 28.82 MWh, by retrofitting electrical appliances is 191.87 MWh, and by retrofitting Heating Ventilation and Air Conditioning is 90.46 MWh. An encouraging reduction of 53.6% in annual energy demand by deploying various energy efficiency measures is projected.KeywordsEnergy efficiency measuresRenewable energyEnergy simulationeQUESTNet Zero Energy BuildingComposite climate zone
The overall energy consumption due to ICT equipment has followed an increasing trend over the last years. A considerable fraction of the consumed energy is caused by user devices, such as Personal Computers (PCs) and displays. However, a large part of this energy is wasted due to an inefficient use. Users leave their PCs on for long periods even when unused, especially in workplaces. Hence, significant energy savings could be achieved just turning them off. However, it is not wise to rely on user collaboration, and, thus, automated tools are needed. In this paper, we present E-Net-Manager, a power management system for large environments, which turns unused PCs off and switches them on when the user is about to use them. To this end, E-Net-Manager leverages soft sensors, i.e., software/hardware tools already in use by the users, thus not introducing any additional cost. E-Net-Manager combines information provided by the users and data obtained from a number of these soft sensors. This way, it is possible to accurately determine the user presence/activity near her/his PC and, therefore, eliminate wastes also due to short periods of inactivity.
Practical energy auditing in offices poses several challenges unlike homes e.g. physically large space, diverse energy appliances types, several appliance instances and occupancy non-obstructiveness. However, improved energy-auditing measures using predictive analytics can benefit energy savings and reduce building operational costs. A review of some publicly available energy datasets such as AMPds, BLUED, ECO, REDD etc. is presented to help understand practical limitations in relying and applying them alike for office plug load audits. A possible approach to predict miscellaneous electrical plug loads (MELs) is proposed using Office Plug Load Dataset (OPLD) based on empirical characteristics and measurements of MELs devices. This work in progress study is one of the first attempts to characterize office desktop appliances across multiple states through a very large experimental dataset. The dataset might be effective in identifying individual appliances & its states in aggregate signature. This can find promising application in improving our understanding on office MELs and thus disaggregating them from single-point measurement.
