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| Content Provider | The American Society of Mechanical Engineers (ASME) Digital Collection |
|---|---|
| Author | Feng, Cong Zhang, Jie |
| Copyright Year | 2018 |
| Abstract | Effective short-term load forecasting (STLF) plays an important role in demand-side management and power system operations. In this paper, STLF with three aggregation strategies are developed, which are information aggregation (IA), model aggregation (MA), and hierarchy aggregation (HA). The IA, MA, and HA strategies aggregate inputs, models, and forecasts, respectively, at different stages in the forecasting process. To verify the effectiveness of the three aggregation STLF, a set of 10 models based on 4 machine learning algorithms, i.e., artificial neural network, support vector machine, gradient boosting machine, and random forest, are developed in each aggregation group to predict 1-hour-ahead load. Case studies based on 2-year of university campus data with 13 individual buildings showed that: (a) STLF with three aggregation strategies improves forecasting accuracy, compared with benchmarks without aggregation; (b) STLF-IA consistently presents superior behavior than STLF based on weather data and STLF based on individual load data; (c) MA reduces the occurrence of unsatisfactory single-algorithm STLF models, therefore enhancing the STLF robustness; (d) STLF-HA produces the most accurate forecasts in distinctive load pattern scenarios due to calendar effects. |
| Sponsorship | Design Engineering Division Computers and Information in Engineering Division |
| File Format | |
| ISBN | 9780791851753 |
| DOI | 10.1115/DETC2018-86084 |
| Volume Number | Volume 2A: 44th Design Automation Conference |
| Conference Proceedings | ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference |
| Language | English |
| Publisher Date | 2018-08-26 |
| Publisher Place | Quebec City, Quebec, Canada |
| Access Restriction | Subscribed |
| Subject Keyword | Support vector machines Load forecasting Algorithms Power systems (machinery) Artificial neural networks Machine learning Robustness Structures Machinery Stress |
| Content Type | Text |
| Resource Type | Article |
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