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QBUS2820 Assignment 2: Forecasting Solar Power Production
Forecasting Solar Power Production
QBUS2820 Assignment 2: Forecasting Solar Power Production
from household Solar Panels
Overview
The assignment consists of forecasting the time series of solar power productions for 4 households in
Australia, at different levels of temporal frequency (half-hourly, daily, monthly).
You will create and validate a methodology that considers the forecast models seen in the lectures,
compute point, probabilistic forecasts and estimate the errors of the model.
Context and Data
Each time series measures a half-hourly frequency solar power production (KWh) from a solar panel
situated in a rooftop of a house.
The dataset (solar_halfhourly.csv) comes in a csv format, with columns:
• Customer: Integer that identifies the house/solar panel.
• datetime: Date in datetime format.
• value: energy production, measured in KWh.
From this data, you would create several time series, for each Customer, and then forecast them all.
The dataset comes from Ausgrid, a subset from the original 300 clients that were made available. Data
ranges from 2010 to 2013.
Temporal Aggregation
The dataset comes measured at half-hourly frequency. As part of the assignment, you will forecast the
half-hourly time series, but you will also generate time series at lower frequencies (Daily and Monthly)
from the original series. This process is called ‘temporal aggregation’, where a group of observations are
aggregated in some form (usually via the mean or the sum function). The groups are generated
according to lower frequencies, for example, we could move from a half-hourly freq. Time series to a
hourly time series by taking the two measurements for each hour in the day, and computing the mean
of those two measurement to serve as the measurement for one hour.
In this assignment, the aggregation will be at the levels of Daily frequency (aggregating all observation in
a natural day) and Monthly, aggregating all observations per calendar Month. Finer details such as
daylight saving will be ignored.
A notebook that showcases temporal aggregation is available on canvas in the Assignment 2 module.
Analyzing a time series at different levels of temporal aggregation is typical in industry, and you might
find that the best model at a given aggregation level might not be the best at another level. Interesting
situations such as discrepancies between forecasts at different levels tend to happen. For example, the
half-hourly forecast for the next day might not have a ‘mean’ that coincides with the forecast of the
daily time series... What to do when that happens?
The forecast problem
The ‘forecasts’ that have to be computed and reported are:
• Point forecasts: You will forecast the next 48 half-hours for the half-hourly time series, the next
7 days for the daily time series, and the next month for the monthly time series. If you use a
validation set and test set, pay attention that these have to be adjusted according to each level
of temporal aggregation. You will do this for each time series, so there would be 12 time series
(4 customers x 3 aggregation levels).
• Probabilistic forecasts: For each time series, forecast the 95% prediction intervals.
• Expected Performance: Estimate of the mean absolute error of the predictions for each time
series. This is the estimation of what is going to be the prediction error on the test set, based on
the training, before looking at the test set.
• Actual performance: The prediction error MAE calculated on the actual on a test set. Compare
then actual vs expected performance. This implies creating a proper test set, using the horizon
that depends on the aggregation level, for each time series.
The Comparison problem (Half-hourly vs Daily)
You will compare and discuss the performance of the best model at the half-hourly level for the next day
(horizon=48) vs the performance of the daily forecast for the next day (horizon=1). The comparison can
be in terms of prediction error, overpredictions vs underpredictions, etc. For example,
Based on that comparison and additional experiments, you will discuss if there is a way of using the half-
hourly forecast to improve the quality of the daily forecasts, and vice-versa (using daily forecast to
improve the quality of the half hourly). This is an open-ended question.
You need to do the comparison for one Customer of your choosing. (e.g. half-hourly vs daily time series
from Customer 3) not all four Customers.
Comments and tips on the methodology
There are several time series in the dataset, and you do not need to forecast ‘manually’ by manually
several models and eyeballing the best model. You can of course try whatever exploratory process you
want to get a grasp of the data, but the objective is to create an automatic methodology, that given a
time series, compares several models and chooses the best model for that time series according to some
metric of ‘best’. Then it computes the final forecasts using the best model. This likely implies some for of
loop where you compare models, seasonalities, preprocessings/transformations, etc.
Once the methodology has been designed, then it can be applied to each time series in the dataset.
You can fine tune the methodology, for example by considering the frequency of a time series (special
versions of the methodology for half-hourly, daily and monthly). The models that consider seasonality,
the seasonality should be another parameter that is adjusted given the data, so finding a good
seasonality should be part of the methodology.
Remember to document in writing your methodology, your design decisions: which models to compare,
seasonalities to compare, adjustments and preprocessing (e.g. what to do with too long or too short
series), necessary tradeoffs for computing speed if they become relevant, etc. Explain what are you
doing and why.
Computing Time restrictions
You will aim towards the whole notebook taking less than 10 minutes to run. This might force you to
make some sacrifices in the ‘completeness’ of the methodology (remember that these are always
implicit, otherwise we would be trying every idea possible...). This is a soft restriction, but if the
notebook deviates ‘a lot’ from 10 minutes of running time, it might get penalized.
What you need to submit
• A notebook (.ipynb file) that runs the methodology, creates the forecasts and documents the
decisions and results along the way.
o Divide it into sections and document clearly what you are doing using markdown cells
before the code of each section. You can use one (or more) cells for the methodological
discussion, this should be separated from the cells that clarify technical (programming
parts). Failure to explain/Incomplete sections of the notebook might lead to strong
penalties for those sections (this is, do not just have ‘code’).
o The filename of the notebooks should be ‘STUDENTID_ ASG2_nbook_QBUS2820.ipynb’
o No pdf document is needed, make sure that the notebook is as clear as possible, you
can find many examples online that interweave code and analysis.
• The notebook must have a Forecast Results section where you will report the forecast items
(the four points in the forecast problem section of this document), using plots for the point and
probabilistic forecast and text output for the performance. You will also report on which model
was used to point forecast each series, to get a rough idea of the dynamics of the series (is it
seasonal? Does it have a trend?, etc.).
• The notebook must have a Half-hourly vs Daily Comparison section, where you report on the
performances for that problem (see the Comparison problem section above) and discuss
potential ways of combining the information at these two levels to improve forecasts.
Marking
Percent of the total grade that is dedicated to each part of the assignment.
• Visual aspect of the notebook (10%). Proper sectioning, plots, text and code cells structure, etc.
• Methodology and Results: Point forecast (40%), Probabilistic( 25%), Estimate the error (10%)
• Comparison Half-hourly vs Daily: 15%
• Other errors might penalize the final grade, e.g. Notebook does not run, the format of
submission is not correct, etc.so percentages might vary because of this.
from household Solar Panels
Overview
The assignment consists of forecasting the time series of solar power productions for 4 households in
Australia, at different levels of temporal frequency (half-hourly, daily, monthly).
You will create and validate a methodology that considers the forecast models seen in the lectures,
compute point, probabilistic forecasts and estimate the errors of the model.
Context and Data
Each time series measures a half-hourly frequency solar power production (KWh) from a solar panel
situated in a rooftop of a house.
The dataset (solar_halfhourly.csv) comes in a csv format, with columns:
• Customer: Integer that identifies the house/solar panel.
• datetime: Date in datetime format.
• value: energy production, measured in KWh.
From this data, you would create several time series, for each Customer, and then forecast them all.
The dataset comes from Ausgrid, a subset from the original 300 clients that were made available. Data
ranges from 2010 to 2013.
Temporal Aggregation
The dataset comes measured at half-hourly frequency. As part of the assignment, you will forecast the
half-hourly time series, but you will also generate time series at lower frequencies (Daily and Monthly)
from the original series. This process is called ‘temporal aggregation’, where a group of observations are
aggregated in some form (usually via the mean or the sum function). The groups are generated
according to lower frequencies, for example, we could move from a half-hourly freq. Time series to a
hourly time series by taking the two measurements for each hour in the day, and computing the mean
of those two measurement to serve as the measurement for one hour.
In this assignment, the aggregation will be at the levels of Daily frequency (aggregating all observation in
a natural day) and Monthly, aggregating all observations per calendar Month. Finer details such as
daylight saving will be ignored.
A notebook that showcases temporal aggregation is available on canvas in the Assignment 2 module.
Analyzing a time series at different levels of temporal aggregation is typical in industry, and you might
find that the best model at a given aggregation level might not be the best at another level. Interesting
situations such as discrepancies between forecasts at different levels tend to happen. For example, the
half-hourly forecast for the next day might not have a ‘mean’ that coincides with the forecast of the
daily time series... What to do when that happens?
The forecast problem
The ‘forecasts’ that have to be computed and reported are:
• Point forecasts: You will forecast the next 48 half-hours for the half-hourly time series, the next
7 days for the daily time series, and the next month for the monthly time series. If you use a
validation set and test set, pay attention that these have to be adjusted according to each level
of temporal aggregation. You will do this for each time series, so there would be 12 time series
(4 customers x 3 aggregation levels).
• Probabilistic forecasts: For each time series, forecast the 95% prediction intervals.
• Expected Performance: Estimate of the mean absolute error of the predictions for each time
series. This is the estimation of what is going to be the prediction error on the test set, based on
the training, before looking at the test set.
• Actual performance: The prediction error MAE calculated on the actual on a test set. Compare
then actual vs expected performance. This implies creating a proper test set, using the horizon
that depends on the aggregation level, for each time series.
The Comparison problem (Half-hourly vs Daily)
You will compare and discuss the performance of the best model at the half-hourly level for the next day
(horizon=48) vs the performance of the daily forecast for the next day (horizon=1). The comparison can
be in terms of prediction error, overpredictions vs underpredictions, etc. For example,
Based on that comparison and additional experiments, you will discuss if there is a way of using the half-
hourly forecast to improve the quality of the daily forecasts, and vice-versa (using daily forecast to
improve the quality of the half hourly). This is an open-ended question.
You need to do the comparison for one Customer of your choosing. (e.g. half-hourly vs daily time series
from Customer 3) not all four Customers.
Comments and tips on the methodology
There are several time series in the dataset, and you do not need to forecast ‘manually’ by manually
several models and eyeballing the best model. You can of course try whatever exploratory process you
want to get a grasp of the data, but the objective is to create an automatic methodology, that given a
time series, compares several models and chooses the best model for that time series according to some
metric of ‘best’. Then it computes the final forecasts using the best model. This likely implies some for of
loop where you compare models, seasonalities, preprocessings/transformations, etc.
Once the methodology has been designed, then it can be applied to each time series in the dataset.
You can fine tune the methodology, for example by considering the frequency of a time series (special
versions of the methodology for half-hourly, daily and monthly). The models that consider seasonality,
the seasonality should be another parameter that is adjusted given the data, so finding a good
seasonality should be part of the methodology.
Remember to document in writing your methodology, your design decisions: which models to compare,
seasonalities to compare, adjustments and preprocessing (e.g. what to do with too long or too short
series), necessary tradeoffs for computing speed if they become relevant, etc. Explain what are you
doing and why.
Computing Time restrictions
You will aim towards the whole notebook taking less than 10 minutes to run. This might force you to
make some sacrifices in the ‘completeness’ of the methodology (remember that these are always
implicit, otherwise we would be trying every idea possible...). This is a soft restriction, but if the
notebook deviates ‘a lot’ from 10 minutes of running time, it might get penalized.
What you need to submit
• A notebook (.ipynb file) that runs the methodology, creates the forecasts and documents the
decisions and results along the way.
o Divide it into sections and document clearly what you are doing using markdown cells
before the code of each section. You can use one (or more) cells for the methodological
discussion, this should be separated from the cells that clarify technical (programming
parts). Failure to explain/Incomplete sections of the notebook might lead to strong
penalties for those sections (this is, do not just have ‘code’).
o The filename of the notebooks should be ‘STUDENTID_ ASG2_nbook_QBUS2820.ipynb’
o No pdf document is needed, make sure that the notebook is as clear as possible, you
can find many examples online that interweave code and analysis.
• The notebook must have a Forecast Results section where you will report the forecast items
(the four points in the forecast problem section of this document), using plots for the point and
probabilistic forecast and text output for the performance. You will also report on which model
was used to point forecast each series, to get a rough idea of the dynamics of the series (is it
seasonal? Does it have a trend?, etc.).
• The notebook must have a Half-hourly vs Daily Comparison section, where you report on the
performances for that problem (see the Comparison problem section above) and discuss
potential ways of combining the information at these two levels to improve forecasts.
Marking
Percent of the total grade that is dedicated to each part of the assignment.
• Visual aspect of the notebook (10%). Proper sectioning, plots, text and code cells structure, etc.
• Methodology and Results: Point forecast (40%), Probabilistic( 25%), Estimate the error (10%)
• Comparison Half-hourly vs Daily: 15%
• Other errors might penalize the final grade, e.g. Notebook does not run, the format of
submission is not correct, etc.so percentages might vary because of this.
案例分类
- 经济Economics
- 金融Finance
- 会计Accounting
- 工商管理Business Management
- 计算机computer science
- 商业分析business analysis
- 法律Law
- 管理Management
- 市场营销Marketing
- 哲学Philosophy
- 心理学Psychology
- 物理Physical
- 统计学statistics
- 电气工程electric engineering
- 化学chemistry
- 生物biology
- 数学mathematics
- 语言学linguistics
- 土木工程civil engineering
- 工程力学engineering mechanics
- 艺术art
标签云
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