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 don'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, consecutevely 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?

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?

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 don'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

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 don'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, consecutevely 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?