IEEE Region 10 Humanitarian Technology Conference 2017 A smart grid prerequisite: survey on electricity demand forecasting models and scope analysis of demand forecasting in Bangladesh Samiul Islam1,2* Amin Ahsan Ali1 Moinul Zaber1 1Data & Design Lab, University of Dhaka, Bangladesh 2BRAC University, Bangladesh *Presenter Funding Source Data Facilitator IEEE Region 10 Humanitarian Technology Conference 2017

IEEE Region 10 Humanitarian Technology Conference 2017 Motivation Chance to work with real data Chance to work in the betterment of the society No research has been performed at this granular level in Bangladesh Characteristics of Big data and it’s prospects IEEE Region 10 Humanitarian Technology Conference 2017

Electricity Sector of Dhaka Electricity distribution is maintained by two organization in Dhaka (North and South) City Corporation DESCO (North) DPDC (South) Zone Sub station Feeder Household Agargaon IEEE Region 10 Humanitarian Technology Conference 2017

Load Data Description Hourly load under each feeder Power factor Handwritten data (Shah Ali Zone) Kalyanpur substation: from 15/05/2015 to 21/06/2016 Shah Ali substation: from 15/05/2015 to 23/10/2016 Digitized data (Shah Ali Zone) Kalyanpur substation: 15/05/2015 ~ 21/05/2015 & 11/12/2015 ~ 17/12/2015 & 01/01/2016 ~ 07/01/2016 Shah Ali Substation: 15/05/2015 ~ 21/05/2015 & 01/12/2015 ~ 21/12/2015 IEEE Region 10 Humanitarian Technology Conference 2017

Load Shedding Data Description Day Date Feeder name Start time End time Duration Cause* of load shedding Load shedding data of Shah Ali substation of Shah Ali zone for year 2015 (digitized) *3 Reasons of load shedding: Preplanned Sudden rise in demand System failure IEEE Region 10 Humanitarian Technology Conference 2017

IEEE Region 10 Humanitarian Technology Conference 2017 Road Map Smart grid and necessity of a study on forecasting techniques Review of recent forecasting scenarios Scope analysis of Bangladesh Legacy method works on an aggregate data Load variability Load shedding patterns and impacts Two short term load forecasting models and performance comparison IEEE Region 10 Humanitarian Technology Conference 2017

Smart Grid and Necessity of a Study on Demand Forecasting Smart Grid (SG) channels a two-way communication between the customers and the utility Collects the data from consumer end and can act accurately Dynamic billing Distribution of the total load over the day to avoid load shedding/blackout What makes a grid smart? Sensors and devices to collect granular level data An efficient and real time data analysis technique to produce optimum decisions How this research can help? How forecasting models work Understanding load variability Seasonal and occasional impacts Reasons and impacts of load shedding Overall, a preparation IEEE Region 10 Humanitarian Technology Conference 2017

Review of Recent Forecasting Scenarios Turkey 40 years No subgrouping Taiwan 1 year Weekday, weekend and holiday Korea 2 weeks Weekday and weekend Spain 1 year Autumn, Winter, Spring and Summer IEEE Region 10 Humanitarian Technology Conference 2017

IEEE Region 10 Humanitarian Technology Conference 2017 Supply/Demand (Max) Balance in a Day: in 2015, available capacity was enough to satisfy maximum demand IEEE Region 10 Humanitarian Technology Conference 2017

IEEE Region 10 Humanitarian Technology Conference 2017 Lost in Translation Issues of legacy method of forecasting Specific field wise data is not sent Aggregate data (hard to understand feeder wise variability) Absence of secondary data sources Policy level Aggregate load Secondary sources Substation Feeder wise record IEEE Region 10 Humanitarian Technology Conference 2017

IEEE Region 10 Humanitarian Technology Conference 2017 Hourly Average Load Comparison of Kalyanpur and Shah Ali Sub Station between Winter and Summer Kalyanpur sub station Shah Ali sub station IEEE Region 10 Humanitarian Technology Conference 2017

Load Shedding Reasons of Shah Ali Zone in 2016 Reasons of load shedding Pre-Scheduled load-shedding proposed by NLDC Sudden rise in demand System failure Sudden rise in demand IEEE Region 10 Humanitarian Technology Conference 2017

Load Shedding Statistics (Weekly and Daily) of Shah Ali Zone in 2016 Comparison Between Different Days in a Week IEEE Region 10 Humanitarian Technology Conference 2017

Forecasting Model 5 input model Data Period: 15th May 2015 to 21st May 2015 (1 week) Training Testing 15th May 2015 to 20th May 2015 (6 days) 21st May 2015 IEEE Region 10 Humanitarian Technology Conference 2017

Predictive output comparison (known set) 5 input model 1600 Actual output Predictive output 1400 1200 Hourly Load (kw) 1000 800 600 400 50 100 150 Hours (Day wise grouped: Friday to Wednesday)

Predictive output comparison (unknown set) 5 input model 1400 Actual output 1300 Predictive output 1200 1100 Hourly Load (kw) 1000 900 800 700 5 10 15 20 25 Hours of 21st May 2015, Thursday

Forecasting Model 8 input model Data Period: 15th May 2015 to 21st May 2015 (1 week) Training Testing 15th May 2015 to 20th May 2015 (6 days) 21st May 2015 IEEE Region 10 Humanitarian Technology Conference 2017

Predictive output comparison (known set) 8 input model 1500 Actual output 1400 Predictive output 1300 1200 1100 1000 Hourly Load (kw) 900 800 700 600 500 50 100 150 Hours (Day wise grouped: Friday to Wednesday)

Predictive output comparison (unknown set) 8 input model 1400 Actual output 1300 Predictive output 1200 1100 Hourly Load (kw) 1000 900 800 700 5 10 15 20 25 Hours of 21st May 2015, Thursday

Performance Analysis 5 input model 8 input model Accuracy*   5 input model 8 input model Accuracy* Dataset known to model: (100-6.4817) = 93.5183% Dataset unknown to model: (100-8.0615) = 91.9385% (100-2.0672) = 97.9328% (100-5.1488) = 94.8512% 𝑀𝐴𝑃𝐸= 100 𝑛 𝑗=1 𝑛 | 𝑦 𝑗 − 𝑦 𝑗 𝑦 𝑗 | * 𝑦 𝑗 & 𝑦 𝑗 are predictive and actual value relatively *For accuracy calculation, MAPE has been used here IEEE Region 10 Humanitarian Technology Conference 2017

IEEE Region 10 Humanitarian Technology Conference 2017 Final Resolution NOT to promote ‘ready to implement’ models To prove that load variability exists and impact of load shedding highly varies even in a small geographical area Taking this into account, a better forecasting can be achieved Load shedding cannot be eliminated, but might be reduced IEEE Region 10 Humanitarian Technology Conference 2017

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