Predicting energy consumption of households
I am not an expert in machine learning, but here is one problem: most of your data is binary. Since you have many such paramters, very little can be derived in terms of correlation between any given parameter and the target quantity. Therefore, statistic methods will have a hard time.

Furthermore, you have a small data set but many parameters. Get rid of some.

Another problem can be that you have mutually exclusive data: for example, the first three parameters (URBAN, RURAL and MOUNTAINOUS) can not be set at the same time. You might want to combine them into one category parameter; thus, the algorithms don't have to find a multi-dimensional anti-correlation and correlate the vector to energy consumption, but use that one parameter directly. Note how this also reduces the number of parameters.
If you can make assumptions about which areas inherently cause more energy consumption, consider turning the parameter into an appropriate interval (of reals). 

As a general rule, machine learning is what you do if other methods don't work. In this case, I don't see the need for machine learning unless you show that reasonable other approaches fail.

For instance: Research energy consumption of the appliances you have parameters for and assume average values resp. values fitting the total area covered. Research and/or make reasonable assumptions for the number of light bulbs, air conditioning, etc based on total area. If this does not already solve your problem, it should reduce the number of parameters with unknown influence.