(Wind Turbine) Photo Credit: https://unsplash.com/photos/ihMzQV3lleo
Source: https://unsplash.com/photos/ihMzQV3lleo

Maximizing energy trading profits: Predicting energy consumption using advanced neural network (Machine Learning — Deep Learning)

Introduction

Objectives

The Dataset

Exploratory Data Analysis and Visualization

Figure A: Corr Analysis Visualization using Seaborn Heatmap
Figure A: Corr Analysis Visualization using Seaborn Heatmap
Figure A: Corr Analysis Visualization using Seaborn Heatmap
Figure B: Correlation Analysis against Wind Energy (“Energy-ile-de-france”)
Figure B: Correlation Analysis against Wind Energy (“Energy-ile-de-france”)
Figure B: Correlation Analysis against Wind Energy (“Energy-ile-de-france”)
Figure C: Corr Analysis against the Highest Corr Forecast in Figure B
Figure C: Corr Analysis against the Highest Corr Forecast in Figure B
Figure C: Corr Analysis against the Highest Corr Forecast in Figure B

Data Analysis — Time Series: Detecting Trend (Seasonality)

Figure D(a): 2017 & 2018 Time Series Analysis for Wind Energy
Figure D(b): 2019 & 2020 Time Series Analysis for Wind Energy
Figure E: 1 Month Time Series Analysis for Wind Energy
Figure E: 1 Month & 1 WeekTime Series Analysis for Wind Energy
Figure F(a): Weekly Time Series Analysis in 1 month (4 weeks)
Figure F(a): Weekly Time Series Analysis in 1 month (4 weeks)
Figure F(b): Weekly Time Series Analysis in 1 month (4 weeks-logarithmic scale)
Figure F(b): Weekly Time Series Analysis in 1 month (4 weeks-logarithmic scale)
Figure G(a) : 3 Months Time Series Analysis for Wind Energy
Figure G(b): 3 Months Time Series Analysis for Wind Energy (logarithmic scale)
Figure G(b): 3 Months Time Series Analysis for Wind Energy (logarithmic scale)

First Trial (Default Test)

Evaluation of Result for the First Trial

Figure H: Training and Test Losses for First Trial
Figure H: Training and Test Losses for First Trial
Figure H: Training and Test Losses for First Trial
Figure I: Actual vs Training / Test Prediction for First Trial
Figure J: Lagged Correlations of First Trial
Figure J: Lagged Correlations of First Trial
Figure K: Profit from our First Trial Model
Figure K: Profit from our First Trial Model
Figure K: Profit from our First Trial Model

Our Approach

Building Data Extractions (data.m)

Figure L: Data Extraction
Figure L: Data Extraction
Figure L: Data Extraction

Feature Engineering (prep.m & pre.m)

Figure M: Normalising formula
Figure M: Normalising formula
Figure M: Normalizing formula

Defining the Network (config.m & network.m)

Figure N: Our Network Layers
Figure N: Our Network Layers

Evaluation of our Model

Figure O: Graph of Training and Test Losses by iteration
Figure O: Graph of Training and Test Losses by iteration
Figure O: Graph of Training and Test Losses by iteration
Figure P: Actual vs Training / Test Prediction of Actual Model
Figure Q: Lagged Correlations of Actual Model
Figure Q: Lagged Correlations of Actual Model
Figure R: Net Profit
Figure R: Net Profit
Figure S: Earnings and Lost Time Series Graph
Figure S: Earnings and Lost Time Series Graph
Figure T: Live Deployment Result
Figure T: Live Deployment Result
Figure T: Live Deployment Result
Figure T: Live Deployment ROI
Figure T: Live Deployment ROI
Figure T: Live Deployment ROI

Further Improvements

Figure U: Time Series of Actual and Forecasted energy vs the Humidity and Weather
Figure U: Time Series of Actual and Forecasted energy vs the Humidity and Weather
Figure U: Time Series of Actual and Forecasted energy vs the Humidity and Weather
Figure V: Weather and Humidity Data during those days that our model made a loss returns
Figure V: Weather and Humidity Data during those days that our model made a loss returns
Figure V: Weather and Humidity Data during those days that our model made a loss returns

Disclaimer of Our Model

Conclusion

Data Enthusiast

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