Keras LSTM: modeling the layers - neural-network

Question

I'm new to Keras LSTM and I building a script for prediction based on time series.
I have the data set below which describe users' power consumption in a year
User mai/13 jun/13 jul/13 ago/13 set/13 out/13 nov/13 dez/13 jan/14 fev/14 mar/14 abr/14
x1 88 99 108 90 86 79 83 75 80 69 75 61
x2 0 143 163 127 174 199 177 140 100 100 130 119
x3 51 50 41 42 51 48 58 53 53 37 47 41
x4 181 211 166 105 128 123 125 98 114 58 4 0
x5 173 187 180 195 211 212 231 188 193 168 166 0
x6 343 321 293 272 400 397 467 392 409 306 408 353
x7 215 150 161 148 153 130 165 166 198 166 150 148
x8 100 140 132 119 121 125 148 135 144 123 124 16
x9 197 193 181 161 201 161 227 154 176 148 171 172
x10 356 371 347 347 363 377 423 373 395 338 337 302
For each user, my script split the data in windows with size n, i.e., if n=6, user x1 will have the following dataset
88 99 108 90 86 79
99 108 90 86 79 83
108 90 86 79 83 75
90 86 79 83 75 80
86 79 83 75 80 69
79 83 75 80 69 75
83 75 80 69 75 61
I want my net to learn from the patterns of each window, so when I'm going to test it, based on a new window, it predicts a value considering the previous values.
Considering these variables:
dataset_length => number of months
dataset_dim => number of users
sequence_length => window size
The net I'm thinking about is:
1st layer: LSTM with sequence_length units, input_shape(None, 1) and return_sequences set true
2nd layer: LSTM with sequence_length*2 units and return_sequences set false
3rd layer: Dense with 1 unit
Is there anything wrong on my net layout? I'm testing the net with a little bit more data and not getting a very good result (high loss). What can I change for a better prediction? Do I need more layers?
Please let me know if you need some piece of code or the test results.