• Disha Harwalkar
• S P Gahana
• Theertha Pilakkal
• Siddharth Ganesh Sreeganesan

The demand for electricity is increasing day by day owing to the growing population and urbanization. Therefore, in order to meet the demand of the consumers and also to ensure no mismatch of supply/demand, development of efficient data forecasting methods is necessary. Load data, solar data, wind data of Germany was used for the purpose of time series forecasting. The load forecasting was done for renewable energy sources- solar, wind and for the conventional load. Linear Regression and LSTM based RNN are the two methods of forecasting used in the paper. The criteria for selecting the best load forecasting method amongst the two was based on the error obtained. Correlation is a statistical technique that can show whether and how strongly paired variables are related. In this paper in order to find the correlation between the load data and solar data, solar data and wind data and load data and wind data Pearson’s and Spearman’s correlation coefficient algorithms were used. The Pearson’s and Spearman’s rank correlation coefficient clearly shows how much energy generated from solar and wind helps in meeting the day to day load requirement. The platform used by us for coding python is SPYDER. For the cross verification of the theoretical and practical values obtained, mathematical calculations were done.

Load forecasting, Correlation, Solar and Wind generation forecasting

"ANALYTICAL STUDY OF CORRELATION BETWEEN DEMAND AND RENEWABLE ENERGY FORECASTING USING DATA MINING/ANALYTICS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 10, page no.72-78, October-2020, Available :http://www.jetir.org/papers/JETIREH06013.pdf

"ANALYTICAL STUDY OF CORRELATION BETWEEN DEMAND AND RENEWABLE ENERGY FORECASTING USING DATA MINING/ANALYTICS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 10, page no. pp72-78, October-2020, Available at : http://www.jetir.org/papers/JETIREH06013.pdf