Solar Intensity Prediction Model for Optimal Energy Utilization
This project proposal focuses on predicting solar intensity up to 48 hours ahead using a model that combines historical data and weather forecasts. By leveraging SVM regression, the model aims to optimize solar power generation for efficient energy consumption. Additionally, the project includes a consumption scheduler and cost-benefit analysis for solar panel installation, offering a comprehensive approach to sustainable energy management.
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Presentation Transcript
PROJECT PROPOSALS FOR CS620 Rajdeep Sardar Mtech 2ndyear student, Department Of Computer Science & Engineering, IIT Bombay.
MTP Work: What IS Achieved Prediction of solar intensity up to 48 hours ahead of time RMSE is in range of 30~35%
OUR MODEL: COMBINE PAST DATA & WEATHER FORECAST Various Past Information [eg. Avg intensity at noon of same week and same month of previous years] Historical Data of Observed Intensity Historical Weather Forecast Data Build Train And Test Data Apply SVM Regression And Predict Solar Intensity n hours ahead [ n <=48] Reference [5]
Road Map Predicting solar power generation over a period in a solar power plant or rooftop solar panel Collecting historical solar generation data Fitting a model to predict the solar power generation in panels in that region.
Project : Consumption Scheduler Total Number Of Appliances Appliance Ratings Customers Electricity Requirement [eg. Wants to wash clothes today] Provide a hour wise Optimized Schedule for the day minimizing cost Electricity tariff Slabs with consumption Customer s Schedule Of Day [week day or weekend, working hours etc] Probable Solar Power Generation
Project : Solar Panel Cost Benefit Model Other Initial Investment Cost [battery, inverter etc] Maintenance Cost Over the lifetime of panel [including extra batteries] Solar Panel Cost A Tool Providing Cost Benefit Analysis For Installing The Panels Probable Solar Power Generation in the lifetime of panel Electricity tariff Slabs with consumptio n Customer Consumption Profile
REFERENCES [1] http://scikit-learn.org/stable/. [2] Cody A. Hill, Matthew Clayton Such, Dongmei Chen, Juan Gonzalez, and W. Mack Grady. Battery energy storage for enabling integration of dis- tributed solar power generation. In 2011 IEEE International Conference in Smart Grid Communications (SmartGridComm), pages 528 533. IEEE, 2011. [3] A. Kansal, J. Hsu, S. Zahedi, and M. Srivastava. Power management in energy harvesting sensor networks. In Transactions on Embedded Computing Systems, 2007. [4] D. Noh, L. Wang, Y. Yang, H. Le, and T. Abdelzaher. Minimum variance energy allocation for a solar-powered sensor system. DCSS, 2009. [5] N. Sharma, P. Sharma, D. Irwin, and P. Shenoy. Predicting solar generation from weather forecasts using machine learning. In 2011 IEEE International Conference in Smart Grid Communications (SmartGridComm), pages 528 533. IEEE, 2011. [6] Navin Sharma, Jeremy Gummeson, David Irwin, , and Prashant Shenoy. Cloudy computing: Leveraging weather forecasts in energy harvesting sen- sor systems. In 2011 IEEE International Conference in Smart Grid Communications (SmartGridComm), pages 528 533. IEEE, 2011.
