Forecasting Applicance Energy Usage 代码代写、R留学生国外代写代做代写python程序

日期：2018-09-16 02:20

Project 1: Forecasting Applicance Energy Usage

1 Introduction

This project studies predictive models for the energy use of appliances. Data used include measurements

of temperature and humidity sensors from a wireless network, weather from a nearby

airport station and recorded energy use of lighting fixtures.

The energy (Wh) data is logged every 10 min for the appliances. The 10 min reporting interval

was chosen to be able to capture quick changes in energy consumption. Another sub-metered load

(lights) is included in the analysis since it has been shown to be a good predictor of room occupancy

when combined with relative humidity measurements. The wireless sensor network’s temperature

and humidity recordings were averaged for the corresponding 10 min periods and merged with the

energy data set by date and time. The time span of the data set is 137 days (4.5 months). The

energy consumption profile shows a high variability. Although there is no weather station outside

the house, weather data for the nearest airport weather station, which is located about 12 km from

the house, is merged by date and time in this study to evaluate its impact on the prediction of the

energy consumption of appliances. The weather data is at hourly intervals, linear interpolation is

used to have a complete data set (at 10 min intervals). The following list presents all the variables or

features. From the date/time variable other extra fea- tures are generated: the number of seconds

from midnight for each day (NSM), the week status (weekend or workday) and the day of the week.

1. date time: year-month-day minute:second

2. Appliances: energy use in Wh (Response to be predicted)

3. lights: energy use of light fixtures in the house in Wh

4. T1: Temperature in kitchen area, in Celsius

5. RH1: Humidity in kitchen area, in %

6. T2: Temperature in living room area, in Celsius

7. RH2: Humidity in living room area, in %

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8. T3: Temperature in laundry room area

9. RH3, Humidity in laundry room area, in %

10. T4: Temperature in office room, in Celsius

11. RH4: Humidity in office room, in %

12. T5: Temperature in bathroom, in Celsius

13. RH5: Humidity in bathroom, in %

14. T6: Temperature outside the building (north side), in Celsius

15. RH6: Humidity outside the building (north side), in %

16. T7: Temperature in ironing room , in Celsius

17. RH7: Humidity in ironing room, in %

18. T8: Temperature in teenager room 2, in Celsius

19. RH8: Humidity in teenager room 2, in %

20. T9: Temperature in parents room, in Celsius

21. RH9: Humidity in parents room, in %

22. To: Temperature outside (from weather station), in Celsius

23. Pressure (from weather station), in mm Hg

24. RHout: Humidity outside (from weather station), in %

25. Windspeed (from weather station): in m/s

26. Visibility (from weather station): in km

27. Tdewpoint (from weather station): Celsius

Since the dataset contains several features or parameters and considering that the airport

weather station is not at the same location as the house, it is also desirable to find out which

parameters are the most important and which ones do not improve the prediction of the appliances’

energy consumption.

2 Dataset

The project has two datasets. The training dataset is for model estimation and model selection. It

includes 14803 samples. Each sample has one output value (i.e., energy use in Wh), and 26 features

defined in the previous section.

The testing dataset has the same format, except that the output values are not provided. You

are expected to predict the output values for each sample in the test dataset. The accuracies of

your prediction will be evaluated based on the numbers you provided.

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3 Project Assignment

Your task is to build a regression model to predict the appliance energy use in Wh for the given 10

mins interval.

3.1 Step 1: Simple Regression Model

In this part, you are required to develop a simple model that can be used for predicting the energy

usage. To reduce the difficulty, you are allowed only limited manipulations of the original data set.

You are allowed to take power transformations of the original variables (square roots, logs, inverses,

squares, etc), but you are NOT allowed to create interaction variables. Your model should include

NO more than 5 predictors/covariates, but should explain as much variability as possible.

After obtaining the model with aforementioned features, you are required to analyze the model

and provide meaningful interpretations. Please focus your attention on the interpretation of the

model. A strong analysis should include the interpretation of various coefficients, statistics, and

plots associated with their model and the verification of any necessary assumptions.

3.2 Step 2: Complex Regression Model

In this part, you are free to construct the “best” regression model for predicting energy consumption.

You are encouraged to experiment with any of the methods that were discussed during the semester

for finding a suitable model. You are allowed to create any new variables you desire (such as

quadratic, interaction, or indicator variables). Your model needs to be estimated based on the

training data, and provides prediction on the testing data. Forecast errors will be evaluated as a

component of your project score.

Note: You are allowed to construct multiple regression models to make the forecasting. Only

the final forecasting results should be submitted for evaluation.

3.3 Step 3: (Optional) Free Form Model

You can choose any arbitrary model, including but not limited to regression models, for prediction

purpose. If you choose to do this part, you need to summarize the method you choose, report the

results, and compare the results with regression models in your report. The forecasting accuracy

from this model will be evaluated. If your accuracy is better than that of the best regression model

in the class, you will be awarded 3 bonus points.

Attention: Make sure your results are replicable by the codes you submitted. Unreproducible

results are considered cheating/plagiarism.