Gas consumption outliers detection - Neural network project. Bad results I tried to detect outliers in the energy gas consumption of some dutch buildings, building a neural network model. I have very bad results, but I can't find the reason. 

I am not an expert so I would like to ask you what I can improve and what I'm doing wrong. This is the complete description: https://github.com/denadai2/Gas-consumption-outliers.

The neural network is a FeedFoward Network with Back Propagation. As described here I splitted the dataset in a "small" dataset of 41'000 rows, 9 features and I tried to add more features. 

I trained the networks but the results have 14.14 RMSE, so it can't predict so well the gas consumptions, consecutively I can't run a good outlier detection mechanism. I see that in some papers that even if they predict daily or hourly consumption in the electric power, they have errors like MSE = 0.01.

What can I improve? What am I doing wrong? Can you have a look of my description?
Just an idea - your data is highly seasonal: daily and weekly cycles are quite perceptible. So first of all, try to decompose your variables (gas and electricity consumption, temperature, and solar radiation). Here is a nice tutorial on time series decomposition for R. 

After obtaining trend and seasonal components, the most interesting part begins. It's just an assumption, but I think, gas and electricity consumption variables would be quite predictable by means of time series analysis (e.g., ARIMA model). From my point of view, the most exiting part here is to try to predict residuals after decomposition, using available data (temperature anomalies, solar radiation, wind speed). I suppose, these residuals would be outliers, you are looking for. Hope, you will find this useful.