HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning

Ivana Kiprijanovska, Simon Stankoski, Igor Ilievski, Slobodan Jovanovski, Matjaž Gams and Hristijan Gjoreski
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Ivana Kiprijanovska: Department of Intelligent Systems, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
Simon Stankoski: Department of Intelligent Systems, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
Igor Ilievski: ITS Iskratel, ITS Softver Centar, 1000 Skopje, North Macedonia
Slobodan Jovanovski: ITS Iskratel, ITS Softver Centar, 1000 Skopje, North Macedonia
Matjaž Gams: Department of Intelligent Systems, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
Hristijan Gjoreski: Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University, 1000 Skopje, North Macedonia

Energies, 2020, vol. 13, issue 10, 1-29

Abstract: Short-term load forecasting is integral to the energy planning sector. Various techniques have been employed to achieve effective operation of power systems and efficient market management. We present a scalable system for day-ahead household electrical energy consumption forecasting, named HousEEC. The proposed forecasting method is based on a deep residual neural network, and integrates multiple sources of information by extracting features from (i) contextual data (weather, calendar), and (ii) the historical load of the particular household and all households present in the dataset. Additionally, we compute novel domain-specific time-series features that allow the system to better model the pattern of energy consumption of the household. The experimental analysis and evaluation were performed on one of the most extensive datasets for household electrical energy consumption, Pecan Street, containing almost four years of data. Multiple test cases show that the proposed model provides accurate load forecasting results, achieving a root-mean-square error score of 0.44 kWh and mean absolute error score of 0.23 kWh, for short-term load forecasting for 300 households. The analysis showed that, for hourly forecasting, our model had 8% error (22 kWh), which is 4 percentage points better than the benchmark model. The daily analysis showed that our model had 2% error (131 kWh), which is significantly less compared to the benchmark model, with 6% error (360 kWh).

Keywords: short-term load forecasting; day ahead; feature extraction; deep residual neural network; multiple sources; electricity (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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