Elucidating Divergent Spatiotemporal Load Dynamics: A Comparative Scrutiny of Urban versus Rural Smart‐Meter Electricity Profiles in the Kashmir Valley
Main Article Content
Qawsain Hussain
This study conducts a comprehensive spatiotemporal analysis of electricity consumption patterns across urban (Lal Chowk) and rural (Kupwara) regions of the Kashmir Valley, integrating hourly load demand with environmental parameters such as temperature, humidity, and solar irradiance over a seven-day observational period. The results delineate distinct consumption behaviors shaped by socio-economic and infrastructural contexts. Urban load profiles exhibit a sharp and pronounced evening peak between 18:00 and 21:00, driven by simultaneous residential and commercial energy use, whereas rural profiles are characterized by dual moderate peaks—morning and evening—aligned with daylight-based activity cycles and limited appliance usage. Correlational analysis reveals a strong positive relationship between ambient temperature and load demand in the urban sector, indicating significant climate-responsive electricity use, particularly for cooling. In contrast, the rural load shows a more muted thermal sensitivity. Solar irradiance further differentiates demand patterns; urban loads remain consistently high throughout the day, while rural consumption displays mid-irradiance peaks and midday declines, reflecting task-based utilization and behavioral adaptations. Furthermore, a focused comparison of event day load (June 6) with regular days illustrates a marginal but notable increase in urban electricity use due to elevated evening activities, while rural demand remains relatively stable. These insights emphasize the critical need for region-specific energy planning, forecasting, and policy interventions in the context of evolving smart grid infrastructure and heterogeneous demand landscapes.
Ahmad, S., & Parray, J. A. (2021). Renewable energy integration in Kashmir: Potential and planning for sustainable development. International Journal of Renewable Energy Research (IJRER), 11(3), 1452–1460.
Alahakoon, D., & Yu, X. (2016). Smart electricity meter data intelligence for future energy systems: A survey. IEEE Transactions on Industrial Informatics, 12(1), 425–436. https://doi.org/10.1109/TII.2015.2414355
Ali, R., & Zameer, A. (2020). Role of weather in short-term electricity load forecasting: A case study from the Himalayan region. Energy Reports, 6, 355–363.
Bedi, J., & Toshniwal, D. (2021). Load forecasting using deep learning models. Procedia Computer Science, 167, 2452–2461.
Berg, M., & Kok, K. (2016). Smart grid and smart meter data-driven approaches for energy flexibility. Renewable and Sustainable Energy Reviews, 57, 1639–1650.
Brahmbhatt, M., & Shukla, S. (2022). Comparative analysis of machine learning algorithms for short-term load forecasting. Energy and AI, 8, 100135.
Chakrabarti, S., & Singh, A. (2021). Distributed energy systems in Indian hill states: A policy and technical perspective. Energy Policy, 156, 112410.
Clement, D., & Misra, R. (2019). Analyzing seasonal load trends in smart grid data from Northern India. Journal of Cleaner Production, 230, 1161–1173.
Dutta, S., & Gupta, P. (2020). Demand-side management in smart grids: Challenges and opportunities. Energy Reports, 6, 1002–1011.
Farhangi, H. (2010). The path of the smart grid. IEEE Power and Energy Magazine, 8(1), 18–28. https://doi.org/10.1109/MPE.2009.934876
Faruqi, A., & Hledik, R. (2022). Time-of-use pricing and smart metering: A global review. The Electricity Journal, 35(1), 106963.
Garg, A., & Sinha, S. (2019). A review on solar irradiance forecasting techniques. Renewable and Sustainable Energy Reviews, 108, 481–499.
Ghosh, D., & Tiwari, P. (2021). Climate-resilient energy systems for the Himalayan region. Energy and Climate Change, 2, 100050.
Hernandez, J., & Zhang, W. (2021). Urban versus rural electricity consumption dynamics: Implications for smart grid deployment. Journal of Energy Systems, 12(2), 54–68.
IEA. (2020). Smart Grids and Beyond: Enabling Electricity Systems of the Future. International Energy Agency. https://www.iea.org/reports/smart-grids
Kumar, R., & Sharma, A. (2018). Real-time load forecasting using weather and load features. Energy Informatics, 1(1), 3.
Mir, A. H., & Bhat, R. A. (2020). Smart grid implementation in Kashmir: Technical and regulatory challenges. International Journal of Electrical Power & Energy Systems, 118, 105763.
Parveen, S., & Najar, A. R. (2023). Forecasting the electricity load of rural and urban households in Kashmir using ARIMA. Energy and Environment Journal, 34(5), 907–918.
Raza, M. Q., & Khosravi, A. (2015). A review on artificial intelligence-based load demand forecasting techniques for smart grid and buildings. Renewable and Sustainable Energy Reviews, 50, 1352–1372.
Singh, V., & Gupta, R. (2022). Electricity consumption pattern modeling using IoT-enabled smart meters. Journal of Smart Energy Systems, 9(3), 201–212.
Srinivas, P., & Awasthi, A. (2022). Short-term electric load prediction using LSTM and GRU. Journal of Energy Engineering, 148(6), 04022033.
Tariq, M. A., & Hussain, S. (2017). Smart grid strategy for Himalayan hill regions of India. Renewable Energy Focus, 22, 19–27.
