There are, obviously, times where you want to force the intercept to 0, but a caution: this changes the interpretation of many of the regression statistics that you are looking at. Essentially, by forcing the intercept to be 0, you are saying that the mean is 0 (think of centered/scaled data). This can be especially problematic if you are extrapolating beyond the range of your data.
Another simple way to think of this: think of a plot of height versus weight. You can fit a line to that data. Should that line be forced to go through the (0,0) point? If you have no height, you would not have a weight so it makes sense. However, the linear relationship likely would not hold from the lowest height datapoint to 0. Forcing it to be linear across the entire range can cause problems.
Dan Obermiller