Why aren't my variables correlated? I am currently doing a project on Load forecasting and it is known that in my country the temperature effects the load.      
I have hourly readings of Load and Temperature from the period between 01-Oct-2014 to 01-Mar-2014. Using python I read in the data and plotted Load against Temperature and found that there was no correlation. I know that my data has been collected into the dataframe correctly. I wish to use weather as a variable in order to calculate the load for tomorrow, however I am unsure how this can be done if there is no correlation.  
If it is 'known' that weather affects load but there is no visible correlation between them, are there any other ways where I can analyse the pattern between them in order to utilize weather in my load forecast, or does this mean that they are simply not related?
Below is my plot, Load on the y axis, in Mega-Watts and Temperature on the x axis, in degrees celcius.   

Below is my code:
plt.plot('Temp','Load',data=df,linestyle='',marker='x',markersize=3)

 A: As others here have intimated , scatter plots between the original series can often be useful but of MORE IMPORTANCE is scatter plots conditional on data conditioned for temporal activities. Often one needs to allow for hourly or daily effects (be they stochastic or deterministic ) and latent level shifts/time trends in order to tease out (identify) useful predictor structure for user specified causal variables. 
Removing seasonality from a dataset where each 24 hour period of a day is normally or bimodally distributed might also be enlightening/informative leading to models that are hourly based BUT incorporating calendar effects (e.f. daily , day-of-the-month , week-of-the=month et al ).
Additionally it is usually preferable to use degree days as a predictor as high demand can be related to both cold and hot temperatures/weather. 
Additionally anomalies need to be identified and conditioned for in order to clearly /identify/measure the effect of user-specified predictors.
Take a look at my response to similar questions https://stats.stackexchange.com/search?tab=newest&q=user%3a3382%20hourly%20data particularly Time Series Analysis for a Newbie .
If you wish you can post your data in a csv format , I might be able to help further.
