As mentioned before it is sort of hard to justify not using an intercept unless there is strong knowledge that the linear regression line passes through the origin.
However, how fitting the model with and without the intercept affects the residuals is kind of case by case. For example, if the true model that generated the data did have an intercept far from the origin, you would definitely being doing a poor job of modelling the data and your residuals would be much larger for the model without intercept.
To make this point more concrete, consider the following example. Imagine you have noisy data coming from the following model:
then fitting this model with an without intercept would result in the following model fits:
Clearly the red line (with intercept) fits the data better and the blue line (without intercept) does a much poorer job. Thus, the residuals for the blue line should be much more dispersed around 0 than the red lines residuals (see blow).
Now on the other hand, imagine that the true model that generated the data does not have an intercept (so the linear regression passes through zero). Let's say for example
$$y=x$$ with a little bit of noise. Fitting a model with or without intercept should return very similar results (depending on how much data you have):
Resulting in residuals that are basically the same.
So at the end of the day what it really boils down to is the quality of the data and whether or not you really believe the true linear model passes through the origin (i.e. no intercept) or not. Unless there is strong evidence of the latter I would recommend including an intercept as a practical safeguard against my first example above.