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Aggregated short-term load forecasting for heterogeneous buildings using machine learning with peak estimation
Abstract System operations and planning are crucial aspects of power system management. They aim to maintain the equilibrium of electricity supply and demand while ensuring reliable and secure power system operation. Consumers have to pay more for electricity during periods of high peak demand in various sectors. If consumers have knowledge about expected peak load ahead of time, such extra charges could potentially be avoided. Accurate energy demand forecasting, and therefore expected peak load information, not only will help to provide a reliable supply of electricity, but also can be useful in reducing the cost of electricity at the consumer level. In this paper, we develop a comparative study for aggregated short-term load forecasting using different data strategies and compare two prediction levels: predicting the aggregated load using a district-level data set, and performing predictions on a lower level and then aggregating them at the district level. After finding the best forecasting model and strategy, these accurate predictions will help to predict the percentage of peak over a certain subscribed power in the entire district. The results showed that the mean absolute percentage error is between 1.67% and 4.80% depending on the machine learning algorithm and the prediction horizon used.
Aggregated short-term load forecasting for heterogeneous buildings using machine learning with peak estimation
Abstract System operations and planning are crucial aspects of power system management. They aim to maintain the equilibrium of electricity supply and demand while ensuring reliable and secure power system operation. Consumers have to pay more for electricity during periods of high peak demand in various sectors. If consumers have knowledge about expected peak load ahead of time, such extra charges could potentially be avoided. Accurate energy demand forecasting, and therefore expected peak load information, not only will help to provide a reliable supply of electricity, but also can be useful in reducing the cost of electricity at the consumer level. In this paper, we develop a comparative study for aggregated short-term load forecasting using different data strategies and compare two prediction levels: predicting the aggregated load using a district-level data set, and performing predictions on a lower level and then aggregating them at the district level. After finding the best forecasting model and strategy, these accurate predictions will help to predict the percentage of peak over a certain subscribed power in the entire district. The results showed that the mean absolute percentage error is between 1.67% and 4.80% depending on the machine learning algorithm and the prediction horizon used.
Aggregated short-term load forecasting for heterogeneous buildings using machine learning with peak estimation
Bellahsen, Amine (author) / Dagdougui, Hanane (author)
Energy and Buildings ; 237
2021-01-09
Article (Journal)
Electronic Resource
English
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