Major Power Outages Prediction 🔋

By Yi Xing (Ylesia) Wu (xw001@ucsd.edu) & Junyue Lin (junyuelin608@gmail.com)

Framing the Problem

A power outage is defined as the loss of the electrical power network supply to an end user. This occurrence engenders a disruption in the provision of electricity, leading to an absence of power in residences, commercial establishments, and other facilities. Power outages can have different degrees of severity. According to the Department of Energy, major power outages refer to those that impacted at least 50,000 customers or caused an unplanned firm load loss of at least 300 MW.

Knowing whether a power outage event is considered major as early as possible is important for local authorities and organizations to take care of the ramifications of the outage. However, it is unlikely that information about the two criteria for a major outage, the number of people affected and the amount of unplanned loss, will be available right after an outage ends. Thus, determining whether an outage event is major is a crucial problem to be solved by a prediction model.

We will be using a binary classification model to predict whether an outage event is major. Since we want our model to accurately classify both major and non-major events, and our dataset is quite balanced, we will be using accuracy as our metric for examining model performance.

Data Cleaning

Just like what we have done in the previous analysis, which can be found here, we have converted the xlsx file into csv file, removed unnecessary rows and columns, converted the data type of each column as appropriate, and create new pd.Timestamp columns by combining existing columns. In addition, we created our response variable column IS.MAJOR by combining information from CUSTOMERS.AFFECTED and DEMAND.LOSS.MW columns based on the definition of major outages: outages that impacted at least 50,000 customers or caused an unplanned firm load loss of at least 300 MW. Moreover, we created a new column TIME.OF.DAY by getting the hour of the day from column OUTAGE.START. There are missing values in multiple columns, but only a few of them are relevant to our modeling problem: OUTAGE.START, and OUTAGE.DURATION. To handle missing values in relevant columns, we used probabilistic imputation because we wanted to preserve the variance for the relevant columns. Last but not least, we split our dataset into a train set and a test set in the proportion of 3 to 1.

Here are the first few rows of our train set:

  YEAR MONTH U.S._STATE POSTAL.CODE NERC.REGION CLIMATE.REGION ANOMALY.LEVEL CLIMATE.CATEGORY CAUSE.CATEGORY CAUSE.CATEGORY.DETAIL HURRICANE.NAMES OUTAGE.DURATION RES.PRICE COM.PRICE IND.PRICE TOTAL.PRICE RES.SALES COM.SALES IND.SALES TOTAL.SALES RES.PERCEN COM.PERCEN IND.PERCEN RES.CUSTOMERS COM.CUSTOMERS IND.CUSTOMERS TOTAL.CUSTOMERS RES.CUST.PCT COM.CUST.PCT IND.CUST.PCT PC.REALGSP.STATE PC.REALGSP.USA PC.REALGSP.REL PC.REALGSP.CHANGE UTIL.REALGSP TOTAL.REALGSP UTIL.CONTRI PI.UTIL.OFUSA POPULATION POPPCT_URBAN POPPCT_UC POPDEN_URBAN POPDEN_UC POPDEN_RURAL AREAPCT_URBAN AREAPCT_UC PCT_LAND PCT_WATER_TOT PCT_WATER_INLAND OUTAGE.START OUTAGE.RESTORATION DAY.OF.WEEK TIME.OF.DAY DAY.OF.MONTH SEASON IS.MAJOR
574 2010.0 6.0 Indiana IN RFC Central -0.4 normal severe weather thunderstorm NaN 1980.0 9.47 8.18 5.77 7.59 3044801.0 2221596.0 3854047.0 9121925.0 33.37893 24.354465 42.250369 2742789.0 341727.0 18796.0 3103313.0 88.3826 11.0117 0.6057 43130.0 47287.0 0.91209 5.8 5955.0 279927.0 2.12734 2.3 6490590.0 72.44 13.27 1860.0 1646.9 53.7 7.05 1.46 98.369028 1.628226 0.991214 2010-06-18 15:30:00 2010-06-20 00:30:00 4.0 15.0 18.0 summer True
58 2003.0 1.0 Ohio OH ECAR Central 0.9 warm intentional attack vandalism NaN 1440.0 7.4 7.09 4.62 6.4 5609731.0 3846164.0 4666822.0 14123130.0 39.720168 27.233085 33.043822 4791889.0 583171.0 22247.0 5397308.0 88.7829 10.8048 0.4122 43223.0 45858.0 0.94254 1.4 9304.0 494250.0 1.882448 3.5 11434788.0 77.92 12.61 2033.7 1740.1 69.9 10.82 2.05 91.154687 8.845313 1.057422 2003-01-25 14:00:00 2003-01-26 14:00:00 5.0 14.0 25.0 winter False
1489 2016.0 3.0 Washington WA WECC Northwest 1.6 warm intentional attack sabotage NaN 1919.0 9.22 8.48 4.4 7.74 3318889.0 2463677.0 2014125.0 7797125.0 42.565548 31.597249 25.831637 2985799.0 367847.0 29012.0 3382664.0 88.2677 10.8745 0.8577 57796.0 50660.0 1.140861 4.3 3504.0 420809.0 0.832682 0.7 7280934.0 84.05 9.08 2380.0 1487.9 16.7 3.57 0.62 93.208786 6.791214 2.405397 2016-03-10 04:00:00 2016-03-11 11:59:00 3.0 4.0 10.0 spring False
900 2011.0 11.0 Wyoming WY WECC West North Central -1.0 cold intentional attack vandalism NaN 0.0 9.54 7.75 5.72 6.83 237488.0 387208.0 904115.0 1528811.0 15.534163 25.327395 59.138442 258528.0 59872.0 9067.0 327467.0 78.9478 18.2834 2.7688 64163.0 47586.0 1.348359 -0.7 917.0 36421.0 2.517778 0.4 567768.0 64.76 40.25 1876.2 1757.6 2.0 0.2 0.13 99.263902 0.736098 0.736098 2011-11-04 10:46:00 2011-11-04 10:46:00 4.0 10.0 4.0 fall False
239 2006.0 2.0 California CA WECC West -0.6 cold severe weather winter storm NaN 2645.0 13.45 11.47 9.57 11.72 6390806.0 8585658.0 3962595.0 19005521.0 33.62605 45.174547 20.849705 12689438.0 1751882.0 79036.0 14520869.0 87.3876 12.0646 0.5443 54508.0 48909.0 1.114478 2.7 29047.0 1963442.0 1.479392 11.9 36021202.0 94.95 5.22 4303.7 2124.1 12.7 5.28 0.59 95.164177 4.835823 1.730658 2006-02-27 18:25:00 2006-03-01 14:30:00 0.0 18.0 27.0 winter True

Here are the first few rows of our test set:

  YEAR MONTH U.S._STATE POSTAL.CODE NERC.REGION CLIMATE.REGION ANOMALY.LEVEL CLIMATE.CATEGORY CAUSE.CATEGORY CAUSE.CATEGORY.DETAIL HURRICANE.NAMES OUTAGE.DURATION RES.PRICE COM.PRICE IND.PRICE TOTAL.PRICE RES.SALES COM.SALES IND.SALES TOTAL.SALES RES.PERCEN COM.PERCEN IND.PERCEN RES.CUSTOMERS COM.CUSTOMERS IND.CUSTOMERS TOTAL.CUSTOMERS RES.CUST.PCT COM.CUST.PCT IND.CUST.PCT PC.REALGSP.STATE PC.REALGSP.USA PC.REALGSP.REL PC.REALGSP.CHANGE UTIL.REALGSP TOTAL.REALGSP UTIL.CONTRI PI.UTIL.OFUSA POPULATION POPPCT_URBAN POPPCT_UC POPDEN_URBAN POPDEN_UC POPDEN_RURAL AREAPCT_URBAN AREAPCT_UC PCT_LAND PCT_WATER_TOT PCT_WATER_INLAND OUTAGE.START OUTAGE.RESTORATION DAY.OF.WEEK TIME.OF.DAY DAY.OF.MONTH SEASON IS.MAJOR
60 2003.0 4.0 Wisconsin WI MRO East North Central 0.0 normal intentional attack vandalism NaN 1219.0 8.79 7.15 4.73 6.65 1491193.0 1504236.0 2060297.0 5055727.0 29.495125 29.75311 40.751745 2446109.0 301434.0 5704.0 2753247.0 88.8445 10.9483 0.2072 43553.0 45858.0 0.949736 2.4 4616.0 238635.0 1.934335 1.9 5479203.0 70.15 14.35 2123.3 1671.5 32.5 3.47 0.9 82.689019 17.312508 3.049041 2003-04-28 15:41:00 2003-04-29 12:00:00 0.0 15.0 28.0 spring False
1055 2012.0 10.0 New Jersey NJ NPCC Northeast 0.3 normal severe weather hurricanes Sandy 11337.0 15.17 12.13 9.98 12.9 1846305.0 2995476.0 621545.0 5486658.0 33.650813 54.595639 11.328299 3455302.0 489943.0 12729.0 3957980.0 87.2996 12.3786 0.3216 55571.0 48156.0 1.153979 1.5 9159.0 493246.0 1.856883 3.0 8874893.0 94.68 2.44 2851.2 1446.5 105.5 39.7 2.01 84.305858 15.682678 4.99828 2012-10-29 16:03:00 2012-11-06 12:00:00 0.0 16.0 29.0 fall True
267 2006.0 7.0 Connecticut CT NPCC Northeast 0.1 normal severe weather thunderstorm NaN 145.0 16.41 14.1 11.86 14.82 1454521.0 1321827.0 450441.0 3246923.0 44.796905 40.710143 13.872857 1437836.0 152984.0 5361.0 1596183.0 90.0796 9.5844 0.3359 67400.0 48909.0 1.378069 3.0 3797.0 237075.0 1.601603 1.5 3517460.0 87.99 3.16 1721.9 1272.4 142.3 37.72 1.83 87.353419 12.646581 3.084972 2006-07-18 20:07:00 2006-07-18 22:32:00 1.0 20.0 18.0 summer False
111 2004.0 2.0 New York NY NPCC Northeast 0.3 normal public appeal NaN NaN 2400.0 14.02 12.07 6.98 11.93 4171308.0 6174482.0 1729385.0 12287262.0 33.94823 50.251081 14.074616 6794431.0 981964.0 10132.0 7786682.0 87.2571 12.6108 0.1301 55866.0 47037.0 1.187703 3.1 20000.0 1071033.0 1.867356 7.7 19171567.0 87.87 5.21 4161.4 1700.0 54.6 8.68 1.26 86.38255 13.61745 3.645862 2004-02-14 20:00:00 2004-02-16 12:00:00 5.0 20.0 14.0 winter False
724 2011.0 5.0 Michigan MI RFC East North Central -0.4 normal intentional attack vandalism NaN 200.0 13.37 10.6 7.39 10.36 2378750.0 3136896.0 2664590.0 8180730.0 29.077478 38.34494 32.571543 4249136.0 521322.0 12961.0 4783420.0 88.8305 10.8985 0.271 39953.0 47586.0 0.839596 2.5 8716.0 394564.0 2.209021 3.6 9876589.0 74.57 8.19 2034.1 1390.4 47.5 6.41 1.03 58.459995 41.540005 2.068987 2011-05-04 12:20:00 2011-05-04 15:40:00 2.0 12.0 4.0 spring False

Prediction Problem: Classification

We are doing a binary classification to classify whether a power outage is major, which means CUSTOMERS.AFFECTED is greater than or equal to 50,000 and DEMAND.LOSS.MW is greater than or equal to 300. We will be working with different models and comparing their performances on the prediction task.

Response Variable

The response variable, IS.MAJOR, is a binary variable indicating whether a power outage is major or not. It has two possible values: True for being a major outage event and False for not being a major outage event.

Justification for Response Variable

We choose to classify whether a power outage is major because understanding the severity of an outage in real-time is crucial for local authorities and organizations to make informed decisions and handle the ramifications of the events.

Features

Using CAUSE.CATEGORY as the only feature for prediction can achieve an accuracy of 85%-90% on the test set, but once it is used along with other features, it overshadows all other features. We will not be using CAUSE.CATEGORY as one of our features in both the baseline and the final models because we want to build a model that takes into account more factors, even if the other features will not have a performance that is as impressive as using only CAUSE.CATEGORY. Also, information about CAUSE.CATEGORY might not be immediately available right after the outage ends (time of prediction). We will be exploring other features that are available since we are interested in predicting whether an outage was major right after it ended. At the time of prediction, we will not be able to immediately count the number of people affected or the amount of loss. Instead, we only have access to real-time information related to the outage, such as the aggregate data of local customers, and basic information about the specific outage, such as the time the outage started and how long it lasted.

Metric for Evaluation

To evaluate the model’s performance, we could have chosen metrics such as precision or recall. However, our dataset is balanced in terms of our response variable. What’s more, we are interested in correctly identifying both outages that are major and non-major. Mistakenly classifying a major outage as non-major might result in inadequate response to significant aftermath events, while mistakenly classifying a non-major outage as major might lead to unnecessary resource allocation. Thus, we decided to use accuracy as our metric for evaluation. In our case, accuracy is the proportion of correctly classified outages out of all outages. On the other hand, precision measures the proportion of actual major outages out of all outages that are classified as major, and recall measures the proportion of correctly identified major outages out of all actual major outages, both of these aspects are of less interest to our problem.

Baseline Model

Model Description

The model used in this prediction task is a logistic regression model. The selected features for the model are OUTAGE.DURATION, and TIME.OF.DAY. We standardized the OUTAGE.DURATION feature and binned the TIME.OF.DAY feature into intervals during pre-processing.

Features

Encoding

Model Performance

For the testing set, the model achieved an accuracy of 67.97%, a precision of 68.06%, and a recall of 67.97%.

Metric Score
Accuracy 67.97%
Precision 68.06%
Recall 67.97%

In our dataset, 53% of the observations are major outages, whereas around 46% are not.

IS.MAJOR Probability
True 0.532595
False 0.467405

We think the accuracy score is not high enough because, if the model predicts all outages to be true, it will have an accuracy of around 53%. The accuracy we have right now is not very big of an improvement from 53%. The recall, accuracy, precision scores are not much different from each other since our dataset is pretty balanced.

Summary

Considering the low accuracy score from our model, there is certainly room for improvement. To improve our model, we will conduct further exploratory analysis to look for additional features for our model, experiment with different classification algorithms, and fine-tune the hyperparameters.

Final Model

Model Choosing and Features

After conducting several trials, we have decided to use the random forest classifier as our model for two main reasons. Firstly, although logistic regression performs well as a baseline model, it has a limited number of tunable hyperparameters compared to other models. This makes it challenging for us to fine-tune the final model effectively. Secondly, our dataset contains numerous categorical features, suggesting that a classifier may be a better choice. Here are the features we have chosen for our model:

Model Performance

Metric Score
Accuracy 77.86%
Precision 77.85%
Recall 77.86%

Summary

The random forest classifier yielded promising results for our prediction task. It enhanced the overall accuracy, precision, and recall of our model. Model selection is a crucial step in this process, and fine-tuning hyperparameters can further enhance model performance.

Fairness Analysis

Accuracy Analysis

For our fairness assessment, we have categorized the test dataset into two groups: power outages happening in spring or fall and those happening in summer or winter. Our primary evaluation metric is accuracy obtained from our fitted final model.

Null Hypothesis

We propose a null hypothesis asserting that our model’s accuracy for determining whether an outage is major is the same between the two groups, with any observed differences attributable to random variability.

Alternative hypothesis

Conversely, our alternative hypothesis is that the model demonstrates unfairness, and there is a significant difference between the accuracy scores of the two groups.

Test Statistic & Significance level

We have selected the absolute difference in accuracy scores between the two season groups as our test statistic, with a significance level of 0.01.

Result

After running a permutation test with 5,000 trials, we obtained a p-value of 0.801, which exceeds our significance level. This outcome leads us to fail to reject the null hypothesis, indicating that our model, based on this accuracy metric, is fair. However, we cannot definitively assert the model’s complete fairness as the permutation test results are also contingent on random chance. Hence, we recommend further testing with more data to verify if the model is ‘truly fair’.

References

https://www.sciencedirect.com/science/article/pii/S2352340918307182

name id minutes contributor_id submitted tags nutrition n_steps steps description ingredients n_ingredients user_id recipe_id date rating review avg_rating calories total_fat_dv sugar_dv sodium_dv protein_dv sat_fat_dv carb_dv
1 brownies in the world best ever 333281 40 985201 2008-10-27 00:00:00 [‘60-minutes-or-less’, ‘time-to-make’, ‘course’, ‘main-ingredient’, ‘preparation’, ‘for-large-groups’, ‘desserts’, ‘lunch’, ‘snacks’, ‘cookies-and-brownies’, ‘chocolate’, ‘bar-cookies’, ‘brownies’, ‘number-of-servings’] [‘138.4’, ‘ 10.0’, ‘ 50.0’, ‘ 3.0’, ‘ 3.0’, ‘ 19.0’, ‘ 6.0’] 10 [‘heat the oven to 350f and arrange the rack in the middle’, ‘line an 8-by-8-inch glass baking dish with aluminum foil’, ‘combine chocolate and butter in a medium saucepan and cook over medium-low heat , stirring frequently , until evenly melted’, ‘remove from heat and let cool to room temperature’, ‘combine eggs , sugar , cocoa powder , vanilla extract , espresso , and salt in a large bowl and briefly stir until just evenly incorporated’, ‘add cooled chocolate and mix until uniform in color’, ‘add flour and stir until just incorporated’, ‘transfer batter to the prepared baking dish’, ‘bake until a tester inserted in the center of the brownies comes out clean , about 25 to 30 minutes’, ‘remove from the oven and cool completely before cutting’] these are the most; chocolatey, moist, rich, dense, fudgy, delicious brownies that you’ll ever make…..sereiously! there’s no doubt that these will be your fav brownies ever for you can add things to them or make them plain…..either way they’re pure heaven! [‘bittersweet chocolate’, ‘unsalted butter’, ‘eggs’, ‘granulated sugar’, ‘unsweetened cocoa powder’, ‘vanilla extract’, ‘brewed espresso’, ‘kosher salt’, ‘all-purpose flour’] 9 386585 333281 2008-11-19 00:00:00 4 These were pretty good, but took forever to bake. I would send it ended up being almost an hour! Even then, the brownies stuck to the foil, and were on the overly moist side and not easy to cut. They did taste quite rich, though! Made for My 3 Chefs. 4 138.4 10 50 3 3 19 6
1 in canada chocolate chip cookies 453467 45 1848091 2011-04-11 00:00:00 [‘60-minutes-or-less’, ‘time-to-make’, ‘cuisine’, ‘preparation’, ‘north-american’, ‘for-large-groups’, ‘canadian’, ‘british-columbian’, ‘number-of-servings’] [‘595.1’, ‘ 46.0’, ‘ 211.0’, ‘ 22.0’, ‘ 13.0’, ‘ 51.0’, ‘ 26.0’] 12 [‘pre-heat oven the 350 degrees f’, ‘in a mixing bowl , sift together the flours and baking powder’, ‘set aside’, ‘in another mixing bowl , blend together the sugars , margarine , and salt until light and fluffy’, ‘add the eggs , water , and vanilla to the margarine / sugar mixture and mix together until well combined’, ‘add in the flour mixture to the wet ingredients and blend until combined’, ‘scrape down the sides of the bowl and add the chocolate chips’, ‘mix until combined’, ‘scrape down the sides to the bowl again’, ‘using an ice cream scoop , scoop evenly rounded balls of dough and place of cookie sheet about 1 - 2 inches apart to allow for spreading during baking’, ‘bake for 10 - 15 minutes or until golden brown on the outside and soft & chewy in the center’, ‘serve hot and enjoy !’] this is the recipe that we use at my school cafeteria for chocolate chip cookies. they must be the best chocolate chip cookies i have ever had! if you don’t have margarine or don’t like it, then just use butter (softened) instead. [‘white sugar’, ‘brown sugar’, ‘salt’, ‘margarine’, ‘eggs’, ‘vanilla’, ‘water’, ‘all-purpose flour’, ‘whole wheat flour’, ‘baking soda’, ‘chocolate chips’] 11 424680 453467 2012-01-26 00:00:00 5 Originally I was gonna cut the recipe in half (just the 2 of us here), but then we had a park-wide yard sale, & I made the whole batch & used them as enticements for potential buyers ~ what the hey, a free cookie as delicious as these are, definitely works its magic! Will be making these again, for sure! Thanks for posting the recipe! 5 595.1 46 211 22 13 51 26
412 broccoli casserole 306168 40 50969 2008-05-30 00:00:00 [‘60-minutes-or-less’, ‘time-to-make’, ‘course’, ‘main-ingredient’, ‘preparation’, ‘side-dishes’, ‘vegetables’, ‘easy’, ‘beginner-cook’, ‘broccoli’] [‘194.8’, ‘ 20.0’, ‘ 6.0’, ‘ 32.0’, ‘ 22.0’, ‘ 36.0’, ‘ 3.0’] 6 [‘preheat oven to 350 degrees’, ‘spray a 2 quart baking dish with cooking spray , set aside’, ‘in a large bowl mix together broccoli , soup , one cup of cheese , garlic powder , pepper , salt , milk , 1 cup of french onions , and soy sauce’, ‘pour into baking dish , sprinkle remaining cheese over top’, ‘bake for 25 minutes or until cheese is lightly browned’, ‘sprinkle with rest of french fried onions and bake until onions are browned and cheese is bubbly , about 10 more minutes’] since there are already 411 recipes for broccoli casserole posted to “zaar” ,i decided to call this one #412 broccoli casserole.i don’t think there are any like this one in the database. i based this one on the famous “green bean casserole” from campbell’s soup. but i think mine is better since i don’t like cream of mushroom soup.submitted to “zaar” on may 28th,2008 [‘frozen broccoli cuts’, ‘cream of chicken soup’, ‘sharp cheddar cheese’, ‘garlic powder’, ‘ground black pepper’, ‘salt’, ‘milk’, ‘soy sauce’, ‘french-fried onions’] 9 29782 306168 2008-12-31 00:00:00 5 This was one of the best broccoli casseroles that I have ever made. I made my own chicken soup for this recipe. I was a bit worried about the tsp of soy sauce but it gave the casserole the best flavor. YUM! 5 194.8 20 6 32 22 36 3
                                The photos you took (shapeweaver) inspired me to make this recipe and it actually does look just like them when it comes out of the oven.                
                                Thanks so much for sharing your recipe shapeweaver. It was wonderful! Going into my family’s favorite Zaar cookbook :)                
412 broccoli casserole 306168 40 50969 2008-05-30 00:00:00 [‘60-minutes-or-less’, ‘time-to-make’, ‘course’, ‘main-ingredient’, ‘preparation’, ‘side-dishes’, ‘vegetables’, ‘easy’, ‘beginner-cook’, ‘broccoli’] [‘194.8’, ‘ 20.0’, ‘ 6.0’, ‘ 32.0’, ‘ 22.0’, ‘ 36.0’, ‘ 3.0’] 6 [‘preheat oven to 350 degrees’, ‘spray a 2 quart baking dish with cooking spray , set aside’, ‘in a large bowl mix together broccoli , soup , one cup of cheese , garlic powder , pepper , salt , milk , 1 cup of french onions , and soy sauce’, ‘pour into baking dish , sprinkle remaining cheese over top’, ‘bake for 25 minutes or until cheese is lightly browned’, ‘sprinkle with rest of french fried onions and bake until onions are browned and cheese is bubbly , about 10 more minutes’] since there are already 411 recipes for broccoli casserole posted to “zaar” ,i decided to call this one #412 broccoli casserole.i don’t think there are any like this one in the database. i based this one on the famous “green bean casserole” from campbell’s soup. but i think mine is better since i don’t like cream of mushroom soup.submitted to “zaar” on may 28th,2008 [‘frozen broccoli cuts’, ‘cream of chicken soup’, ‘sharp cheddar cheese’, ‘garlic powder’, ‘ground black pepper’, ‘salt’, ‘milk’, ‘soy sauce’, ‘french-fried onions’] 9 1.19628e+06 306168 2009-04-13 00:00:00 5 I made this for my son’s first birthday party this weekend. Our guests INHALED it! Everyone kept saying how delicious it was. I was I could have gotten to try it. 5 194.8 20 6 32 22 36 3
412 broccoli casserole 306168 40 50969 2008-05-30 00:00:00 [‘60-minutes-or-less’, ‘time-to-make’, ‘course’, ‘main-ingredient’, ‘preparation’, ‘side-dishes’, ‘vegetables’, ‘easy’, ‘beginner-cook’, ‘broccoli’] [‘194.8’, ‘ 20.0’, ‘ 6.0’, ‘ 32.0’, ‘ 22.0’, ‘ 36.0’, ‘ 3.0’] 6 [‘preheat oven to 350 degrees’, ‘spray a 2 quart baking dish with cooking spray , set aside’, ‘in a large bowl mix together broccoli , soup , one cup of cheese , garlic powder , pepper , salt , milk , 1 cup of french onions , and soy sauce’, ‘pour into baking dish , sprinkle remaining cheese over top’, ‘bake for 25 minutes or until cheese is lightly browned’, ‘sprinkle with rest of french fried onions and bake until onions are browned and cheese is bubbly , about 10 more minutes’] since there are already 411 recipes for broccoli casserole posted to “zaar” ,i decided to call this one #412 broccoli casserole.i don’t think there are any like this one in the database. i based this one on the famous “green bean casserole” from campbell’s soup. but i think mine is better since i don’t like cream of mushroom soup.submitted to “zaar” on may 28th,2008 [‘frozen broccoli cuts’, ‘cream of chicken soup’, ‘sharp cheddar cheese’, ‘garlic powder’, ‘ground black pepper’, ‘salt’, ‘milk’, ‘soy sauce’, ‘french-fried onions’] 9 768828 306168 2013-08-02 00:00:00 5 Loved this. Be sure to completely thaw the broccoli. I didn't and it didn't get done in time specified. Just cooked it a little longer though and it was perfect. Thanks Chef. 5 194.8 20 6 32 22 36 3