Hi all,
Very new to this so please excuse my errors.
I want to create an agent based model using the mesa package (Python 3.6.3)
to model solar generation, electricity use and grid exports. Ultimately I
would like agents (households) to interact (ie trading energy between
homes).
For the first iteration of the model I want to show outcomes **without**
interaction between agents, using a sample of real consumption and
generation data. I.e. for each step (i), each household (j) will
generate/use electricity based on the real world data series, i.e.,
indexing the corresponding consumption and generation values for that
particular household at time period i.
Here is a snippet of the dataframes with consumption and generation data
(gen values are all zero because time periods are at night):
>>con
Customer 1 2
0 1 0.287 1.207
1 2 0.278 0.193
2 3 0.051 0.063
>>gen
Customer Capacity 1 2 3
0 1 3.78 0.0 0.0 0.0
1 2 1.62 0.0 0.0 0.0
2 3 1.00 0.0 0.0 0.0
Column headings refer to time periods (t=1, t=2, etc). Capacity = battery
capacity (which I will ignore for the moment).
How do I define a 'step' such that for each customer number (unique id) and
time period, the model indexes the appropriate consumption and generation
values for that time period, and adds them to that agent? The correct row
will always be the customer's unique id - 1, but the column will change
with each step (i.e. for customer 1, at t=1 the consumption value should be
0.287, at t=2 it should be 1.207 and so on.
Here is a sample of my code so far:
class PowerAgent(Agent):
def __init__(self, unique_id, model):
super().__init__(unique_id, model)
self.use = 0
self.gen = 0
def step(self):
self.use += con.iloc[unique_id-1, <<code to show that for the
ith step, we should index column i +2 >>]
self.gen += gen.iloc[unique_id-1, <<code to show that for the
ith step, we should index column i +1 >> ]
Would appreciate pointers. I'm new to Python and mesa so all help is
welcomed with much gratitude.
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