Abstract

Individual device loads and energy consumption feedback is one of the important approaches for pursuing users to save energy in residences. This can help in identifying faulty devices and wasted energy by devices when left On unused. The main challenge is to identity and estimate the energy consumption of individual devices without intrusive sensors on each device. Non-intrusive load monitoring (NILM) or energy disaggregation, is a blind source separation problem which requires a system to estimate the electricity usage of individual appliances from the aggregated household energy consumption. In this paper, we propose a novel deep neural network-based approach for performing load disaggregation on low frequency power data obtained from residential households. We combine a series of one-dimensional Convolutional Neural Networks and Long Short Term Memory (1D CNN-LSTM) to extract features that can identify active appliances and retrieve their power consumption given the aggregated household power value. We used CNNs to extract features from main readings in a given time frame and then used those features to classify if a given appliance is active at that time period or not. Following that, the extracted features are used to model a generation problem using LSTM. We train the LSTM to generate the disaggregated energy consumption of a particular appliance. Our neural network is capable of generating detailed feedback of demandside, providing vital insights to the end-user about their electricity consumption. The algorithm was designed for low power offline devices such as ESP32. Empirical calculations show that our model outperforms the state-of-the-art on the Reference Energy Disaggregation Dataset (REDD)

Abstract

The radiant systems consume less energy and provide better thermal comfort as compared to the conventional system, but the main drawback of radiant technology is inability to cater the latent load and condensation. This problem has engaged researchers to push their emphasis on Personalized Conditioning System (PCS). It forms a micro-climate region near the occupant. This study is aimed at PCS combined with a radiant conditioning system, which evaluates the performance of the PCS and compares it with different radiant cooling systems. A radiant cubicle was used to investigate the performance of the PCS for both heating & cooling modes. For heating mode, the Standard Effective Temperature (SET) was calculated with the help of thermal comfort surveys which were in a range of 23.4°C to 27.3°C. For cooling mode, four different cases compared the performance of the system which are: Conventional Air Conditioning System (CACS), Radiant Cooled Ceiling System (RCCS), Personalized Radiant Conditioning System (PRCS), and both PRCS-RCCS coupled. It was observed that PRCS alone reaches the thermal comfort criteria with low energy consumption. The energy-saving of RCCS-PRCS was achieved 38% when compared with CACS. It can be concluded that PRCS is a new step in the field of thermal comfort.

Abstract

Reducing electricity consumption has emerged as a key area of concern according to the National Mission of Sustainable Habitat of Government of India. Among the Indian cities, Delhi’s electricity consumption is the highest. This paper aims to evaluate the impact of UHI on electricity consumption of Delhi. The study utilizes the satellite land surface temperature (LST) data to compute the district-wise urban heat island intensity (UHII) in Delhi. The electricity consumption and LST data are used to examine the association between them. The scatter plots between percapita net electricity consumption and night-time LST of each district show strong association between them and polynomial curves best fit this association. The equations of these polynomial curves are then used to estimate the electricity consumption of each district assuming their LSTs to be equal to that of the surrounding rural strip (i.e. without UHI effect). The difference between the observed and estimated electricity consumption at observed and assumed (i.e. rural) LSTs provided the increase in electricity consumption due to UHI. The study shows that UHI formation caused an increased electricity consumption of 2600 GWh (i.e. 11.4%) annually on an average over eight districts of Delhi during the period April 2012 to March 2017.

Abstract

This paper presents a systematic review on thermal comfort studies in Indian residential buildings, to identify the present research scenario, data gaps and policy interventions. Majority of the studies are performed in composite climate (ten), followed by warm-humid (seven) and a very few from cold (two) and hot-dry (two) climates. None thermal comfort study is found in the temperate climate. Seven studies have considered multiple climates for assessment of thermal comfort in residential buildings. This shows that thermal comfort studies in Indian residential buildings are scarce, scattered and unorganized. Further, due to differences in socio-cultural set-up and local adaptations, the prodigious variations in occupant’s comfort requirements are reported. This review argues for the dynamic modifications in individual behaviours due to change in cost of building energy services and comfort requirements. Only four studies have partially considered the occupant behaviour regarding control of indoor thermal environment. The results obtained from these studies indicate that there is strong need of localised thermal comfort model that will not only help in improving comfort requirements but also building energy performance. Importantly this review

Abstract

Residensial Energy electricity

Abstract

Load identification is an essential step in Non-Intrusive Load Monitoring (NILM), a process of estimating the power consumption of individual appliances using only whole-house aggregate consumption. Such estimates can help consumers and utility companies improve load management and save power. Current state-of-the-art methods for load identification generally use either steady state or transient features for load identification. We hypothesize that these are complementary features and so a hybrid combination of them will result in an improved appliance signature. We propose a novel hybrid combination that has the advantage of being low-dimensional and can thus be easily integrated with existing classification models to improve load identification. Our improved hybrid features are then used for building appliance identification models using Naive Bayes, KNN, Decision Tree and Random Forest classifiers. The proposed NILM methodology is evaluated for robustness in changing environments. An automated data collection setup is established to capture 7 home appliances aggregate data under varying voltages. Experimental results show that our proposed feature fusion based algorithms are more robust and outperform steady state and transient feature-based algorithms by at least +9% and +15% respectively.

Abstract

This paper presents a robust short-term electrical load forecasting framework that can capture variations in building operation, regardless of building type and location. Nine different hybrids of recurrent neural networks and clustering are explored. The test cases involve five commercial buildings of five different building types, i.e., academic, research laboratory, office, school and grocery store, located at five different locations in Bangkok-Thailand, Hyderabad-India, Virginia-USA, New York-USA, and Massachusetts-USA. Load forecasting results indicate that the deep learning algorithms implemented in this paper deliver 20–45% improvement in load forecasting performance as compared to the current state-of-the-art results for both hour-ahead and 24-ahead load forecasting. With respect to sensitivity analysis, it is found that: (i) the use of hybrid deep learning algorithms can take as less as one month of data to …

Abstract

Rapid urbanization and associated land-use changes in cities cause an increase in the demand for electricity by altering the local climate. The present study aims to examine the variations in total energy and cooling energy demand in a calibrated building, caused by urban heat island formation over Delhi. The study used Sentinel-2A multispectral imagery for land use and land cover (LULC) of mapping of Delhi, and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery for land surface temperature (LST) mapping during March 2018. It was observed that regions with dense built-up areas (ie, with built-up area greater than 90%) had a higher annual land surface temperature (LST), ie, 293.5 K and urban heat island intensity (UHII) ranging from 0.9 K-5.9 K. In contrast, lower annual values of LST (290K) and UHII (0.0-0.4 K) were observed in regions with high vegetation cover (53%). Statistical analysis reveals that a negative correlation exists between vegetation and nighttime LST, which is further confirmed by linear regression analysis. Energy simulations were performed on a calibrated building placed at three different sites, identified on the basis of land use and land cover percentage and annual LST. Simulation results showed that the site located in the central part of Delhi displayed higher annual building energy consumption (425.12 MWh/y) compared to the site located in the rural periphery (381.13 MWh/y). For all the three sites, the maximum electricity consumption and the cooling load were observed in the summer season, while the minimum were seen in the winter season. The study indicates that UHI formation leads to …

Abstract

Residential energy (electricity) use in India is expected to grow four-fold by 2030, yet there is paucity of data that is essential for developing energy policy or programme. This paper undertakes a meta-study of residential energy studies in India to characterise their scope, data collection methods and findings. An in-depth review of literature was undertaken using Google Scholar, Web of Science, Scopus and ScienceDirect to identify relevant journal publications, conference papers and reports. A series of expert workshops were held to identify any data gaps and stakeholders’ needs. The identified studies were characterised using a meta-data framework that included criteria such as climate, dwelling form, income group, methods and study duration. An online searchable platform (REACT) was created to make the identified studies more accessible using the meta-data criteria. Of the 29 studies identified, only 16 were published in peer-reviewed publications (10 in journals and 6 in conferences proceedings). About 11 studies focussed on dwelling-level energy use, while the remaining used econometric data and statistical modelling at a macro-scale. To develop evidence based energy policies, it is vital to gather empirical data on residential energy in different climatic zones. Robust data sharing policies should be implemented to avoid duplication of effort.

Abstract

Personalized Comfort Systems (PCS) have been seen as an effective alternative to provide thermal comfort along with reducing energy consumption. This paper contains the methodology and results of a study conducted in a laboratory environment that aims to evaluate Thermally Activated Furniture (TAF) panels. These panels mounted in front and on the side of a workstation as space partition panels, can provide thermal comfort and have the potential to save energy. The experiments were carried out in a controlled thermal comfort chamber using a thermal mannequin having 22 body segments and TAF. A total of 15 cases were examined against a reference case with an aim to understand the impact of TAF on the change in PMV, Skin Temperature (Tskin) and Heat Flux (U) of 22 individual body segments and change in the energy consumption. The combinations of three Room Air Temperatures (Tair) i.e. 26
C, 28
C and 30
C were tested with a combination of four surface temperature (Tsur) ranging from the difference between Tair and Tsur 2 to 8
C. For the entire study, Relative Humidity (RH) was maintained at 50%. The results indicate a change in PMV by about 0.15 votes towards the cooler side when the difference between Tair and Tsur was D2
C for the three Tair cases. The PMV changed by about 0.5 votes in the case of the D8
C difference between Tair and Tsur. The increase in DT values was found to increase the cooling thermal energy consumption relative to the baseline case without having PCS in operation, whereas the increase in Tair caused a decrease in the cooling thermal energy consumption