BL40A2010 Introduction to IoT-Based SystemsAssignment 1, 8.9.2023 - Janette Koenkytö

(1) Analyze an electric heater as a system following Example 1.1.

Answer: OP:electric current passes through resistor and it create heat from current. C1:Joule heating makes sure it works C2:clean dust C3: it warns the air close to it surfet and so on it warms a room for example.

(2) Give one example for each scientific rationalities presented in Section 1.2.1 similar to Example 1.2.

Answer:earth is flat classical: you can see in air its not /non-classical:sun going around the Earth /interventionist: old maps, made when there was no airplanes or satellites to go to far away into air

(3) Download the air temperature data of one year from FMI at the city/region you currently live. Make four (arbitrary) time series plots for one year, one month, one week and one day. Besides, for each one of these time series, compute the average temperature, and plot it in the respective plot as a constant line.Hint: Code like in the tutorial notebook.In [ ]:

YEAR

import pandas as pd #https://pandas.pydata.org/ import matplotlib.pyplot as plt #https://matplotlib.org/ import matplotlib.dates as mdates #https://matplotlib.org/ import numpy as np #http://www.numpy.org/ Temperature = pd.read_csv("lpr.csv",dayfirst=True,sep=",", header=0,decimal=b".",index_col=0, parse_dates= [[0,1,2,3]],usecols=[0,1,2,3,5]) Temperature.info() len(Temperature[Temperature['Air temperature (degC)'].str.contains('-')]) Temperature = Temperature.replace('-',method='bfill') Temperature = Temperature['Air temperature (degC)'].astype(float) plt.figure(figsize=(16,6)) plt.plot(Temperature,color='blue', marker='x',linestyle=':') plt.ylabel("Temperature in [°C]") plt.grid(True) mean=Temperature.mean() plt.axhline(mean) plt.show()

Run to view results

MONTH

import pandas as pd #https://pandas.pydata.org/ import matplotlib.pyplot as plt #https://matplotlib.org/ import matplotlib.dates as mdates #https://matplotlib.org/ import numpy as np #http://www.numpy.org/ Temperature = pd.read_csv("lpr.csv",dayfirst=True,sep=",", header=0,decimal=b".",index_col=0, parse_dates= [[0,1,2,3]],usecols=[0,1,2,3,5]) Temperature.info() len(Temperature[Temperature['Air temperature (degC)'].str.contains('-')]) Temperature = Temperature.replace('-',method='bfill') Temperature = Temperature['Air temperature (degC)'].astype(float) plt.figure(figsize=(16,6)) plt.plot(Temperature["2023-2"],color='blue', marker='x',linestyle=':') plt.ylabel("Temperature in [°C]") plt.grid(True) mean=Temperature["2023-2"].mean() plt.axhline(mean) plt.show()

Run to view results

WEEK

import pandas as pd #https://pandas.pydata.org/ import matplotlib.pyplot as plt #https://matplotlib.org/ import matplotlib.dates as mdates #https://matplotlib.org/ import numpy as np #http://www.numpy.org/ Temperature = pd.read_csv("lpr.csv",dayfirst=True,sep=",", header=0,decimal=b".",index_col=0, parse_dates= [[0,1,2,3]],usecols=[0,1,2,3,5]) Temperature.info() len(Temperature[Temperature['Air temperature (degC)'].str.contains('-')]) Temperature = Temperature.replace('-',method='bfill') Temperature = Temperature['Air temperature (degC)'].astype(float) plt.figure(figsize=(16,6)) plt.plot(Temperature["2023-2-1":"2023-2-7"],color='blue', marker='x',linestyle=':') plt.ylabel("Temperature in [°C]") plt.grid(True) mean=Temperature["2023-2-1":"2023-2-7"].mean() plt.axhline(mean) plt.show()

Run to view results

DAY

import pandas as pd #https://pandas.pydata.org/ import matplotlib.pyplot as plt #https://matplotlib.org/ import matplotlib.dates as mdates #https://matplotlib.org/ import numpy as np #http://www.numpy.org/ Temperature = pd.read_csv("lpr.csv",dayfirst=True,sep=",", header=0,decimal=b".",index_col=0, parse_dates= [[0,1,2,3]],usecols=[0,1,2,3,5]) Temperature.info() len(Temperature[Temperature['Air temperature (degC)'].str.contains('-')]) Temperature = Temperature.replace('-',method='bfill') Temperature = Temperature['Air temperature (degC)'].astype(float) plt.figure(figsize=(16,6)) plt.plot(Temperature["2023-2-1"],color='blue', marker='x',linestyle=':') plt.ylabel("Temperature in [°C]") plt.grid(True) mean=Temperature["2023-2-1"].mean() plt.axhline(mean) plt.show()

Run to view results

(4) Consider a house with an electric heater. How the electricity consumption of the heater would be affected by the outside air temperature? Discuss about 3 current heating methods and their differences.

Answer: the heater warms the air what comes from window cracks so if it is super cold the heater must warm the air more so the room temperature doesn't cool down. if There is floor electric heater it warms the air and the floor. Air heat pump warms the outside air so when outside temperature is low the pump needs to work more to heat it to wanted temperature. The way they heat is different. The difference about air heat pumps compressor compress the air.

.css-15w88e5{color:var(--chakra-colors-fg-neutral-primary);font-weight:inherit;letter-spacing:-0.09px;}BL40A2010 Introduction to IoT-Based SystemsAssignment 1, 8.9.2023 - Janette Koenkytö

BL40A2010 Introduction to IoT-Based SystemsAssignment 1, 8.9.2023 - Janette Koenkytö