An Investigation of Weather Forecasting using Machine Learning Techniques
Customarily, climate expectations are performed with the assistance of enormous complex models of material science, which use distinctive air conditions throughout a significant stretch of time. In this paper, we studied a climate expectation strategy that uses recorded information from numerous climate stations to prepare basic AI models, which can give usable figures about certain climate conditions for the not so distant future inside a brief timeframe These conditions are frequently flimsy on account of annoyances of the climate framework, making the models give mistaken estimates. The model are for the most part run on many hubs in an enormous High Performance Computing (HPC) climate which burns through a lot of energy.. The modes can be run on significantly less asset serious conditions. In this paper we describe that the sufficient to be utilized status of the workmanship methods. Moreover, we described that it is valuable to use the climate stations information from various adjoining territories over the information of just the region for which climate anticipating is being performed.
Abraham, Ajith, Ninan Sajith Philip, Baikunth Nath, & P. Saratchandran. (2002). Performance analysis of connectionist paradigms for modeling chaotic behavior of stock indices." In: Second International Workshop on Intelligent Systems Design and Applications, Computational Intelligence and Applications, Dynamic Publishers Inc., USA, pp. 181-186.
Goddard, Lisa, Simon J. Mason, Stephen E. Zebiak, Chester F. Ropelewski, Reid Basher, & Mark A. Cane. (2001). Current approaches to seasonal to interannual climate predictions. International Journal of Climatology, 21(9), 1111-1152.
Chaves, Rosane R., Robert S. Ross, & T. N. Krishnamurti. (2005). Weather and seasonal climate prediction for South America using a multi model super ensemble. International Journal of Climatology, 25(14), 1881-1914.
Baboo, S. Santhosh, I., & Kadar Shereef. (2010). An efficient weather forecasting system using artificial neural network. International Journal of Environmental Science and Development, 1(4), 321.
Paras, Sanjay Mathur. (2016). A simple weather forecasting model using mathematical regression. Indian Research Journal of Extension Education, 12(2), 161-168.
N. Hasan, M. T. Uddin, & N. K. Chowdhury. (2016). Automated weather event analysis with machine learning. In: Proc. IEEE2016 International Conference on Innovations in Science, Engineering and Technology (ICISET), pp. 1-5.
L. L. Lai, H. Braun, Q. P. Zhang, Q. Wu, Y. N. Ma, W. C. Sun, & L. Yang. (2004). Intelligent weather forecast. In: Proc. IEEE 2004 International Conference on Machine Learning and Cybernetics, pp. 4216-4221.
A. G. Salman, B. Kanigoro, & Y. Heryadi. (2015). Weather forecasting using deep learning techniques. In: Proc.IEEE2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp. 281-285.
Delhi Weather Data. [Online]. Available at: https://www.kaggle.com/mahirkukreja/delhi-weatherdata/home.
Raspberry Pi. [Online]. Available at: https://en.wikipedia.org/wiki/Raspberry_Pi.
Database of State Incentives for Renewables and Efficiency. (2010). Available at: http://www.dsireusa.org.
State of California Executive Order S-21-09. (2009). Available at: http://gov.ca.gov/executive-order/13269.
Freeing the grid: Best and worst practices in state net metering policies and interconnection procedures. (2009). Available at: http://www.newenergychoices.org/uploads/FreeingTheGrid2009.pdf.
N. Sharma, J. Gummeson, D. Irwin, & P. Shenoy. (2010 June). Cloudy computing: leveraging weather forecasts in energy harvesting sensor systems. In: SECON.
N. Cristianini & J. Shawe-Taylor. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press.
Weather forecast using the sensor data from your IoT hub in Azure Machine Learning. (2018). Available at: https://docs.microsoft.com/en-us/azure/iot-hub/ iot-hub-weather-forecast-machine-learning.
Dan Becker. (2018). Using categorical data with one hot encoding. Available at: https://www.kaggle.com/dansbecker/using-categorical-data-with-one-hot-encoding/.
Jeffrey Burt. (2017). Machine learning storms into climate research. Available at: https://www.nextplatform.com/2017/04/18/machine-learning-storms-climate-research/.
Aditya Grover, Ashish Kapoor, & Eric Horvitz. (2015). A deep hybrid model for weather forecasting. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, pp. 379–386.
Copyright (c) 2021 International Journal of Engineering and Management Research
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.