A scaling law for short term load forecasting on varying levels of aggregation

Sevlian, Raffi; Rajagopal, Ram

doi:10.1016/j.ijepes.2017.10.032

Cited by 87 publications

(56 citation statements)

References 32 publications

(44 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The results showed that forecasting accuracy increases as group size increases, even for small groups. A simple empirical scaling law is proposed in [122] to describe how the accuracy changes as different aggregation levels. The derivation of the scaling law is based on Mean Absolute Percentage Error (MAPE).…”

Section: B Forecasting With Smart Meter Datamentioning

confidence: 99%

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Wang

Chen

Hong

et al. 2019

IEEE Trans. Smart Grid

923

370

View full text Add to dashboard Cite

The widespread popularity of smart meters enables an immense amount of fine-grained electricity consumption data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. How to employ massive smart meter data to promote and enhance the efficiency and sustainability of the power grid is a pressing issue. To date, substantial works have been conducted on smart meter data analytics. To provide a comprehensive overview of the current research and to identify challenges for future research, this paper conducts an application-oriented review of smart meter data analytics. Following the three stages of analytics, namely, descriptive, predictive and prescriptive analytics, we identify the key application areas as load analysis, load forecasting, and load management. We also review the techniques and methodologies adopted or developed to address each application. In addition, we also discuss some research trends, such as big data issues, novel machine learning technologies, new business models, the transition of energy systems, and data privacy and security.

show abstract

Section: B Forecasting With Smart Meter Datamentioning

confidence: 99%

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Wang

Chen

Hong

et al. 2019

IEEE Trans. Smart Grid

923

370

View full text Add to dashboard Cite

show abstract

“…Because of the volatile nature of individual customer, load forecasting at low level is much more challenging, and therefore, the forecasting methods are still in the development stage [13]. The characteristics of individual load and aggregated loads with the Aggregation Error Curve (AEC) are discussed in [15].…”

Section: Introductionmentioning

confidence: 99%

Short-term Load Forecasting at Different Aggregation Levels with Predictability Analysis

Peng

Wang

et al. 2019

2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

View full text Add to dashboard Cite

Short-term load forecasting (STLF) is essential for the reliable and economic operation of power systems. Though many STLF methods were proposed over the past decades, most of them focused on loads at high aggregation levels only. Thus, lowaggregation load forecast still requires further research and development. Compared with the substation or city level loads, individual loads are typically more volatile and much more challenging to forecast. To further address this issue, this paper first discusses the characteristics of small-and-medium enterprise (SME) and residential loads at different aggregation levels and quantifies their predictability with approximate entropy. Various STLF techniques, from the conventional linear regression to state-of-the-art deep learning, are implemented for a detailed comparative analysis to verify the forecasting performances as well as the predictability using an Irish smart meter dataset. In addition, the paper also investigates how using data processing improves individual-level residential load forecasting with low predictability. Effectiveness of the discussed method is validated with numerical results.Index Terms-Deep learning, machine learning, smart meter, short-term load forecasting, load aggregation level, predictability I.

show abstract

“…We can see from Fig. 5 that there is a change in the pattern of the realized 2 Since the residential load from single household is notoriously difficult to be forecasted accurately because of the large randomness, and the forecasting for an aggregation of 100 households is typically a easier task [4], we only use the forecasting results for 10 households as examples in the remaining part of Section IV to illustrate the performance of our approach. For the complete data and code, please refer to https://github.com/zhhhling/June2019.git.…”

Section: B Simulation Resultsmentioning

confidence: 99%

“…Compared to standard load forecasting used in transmission system operations, residential level forecasting have received less attention until relatively recently. For introductions and surveys on this topic the readers can refer to [4]- [6] and the references within. Despite these advances, residential load forecasting, especially for a single or a small number of households, remains a challenging problem for several reasons.…”

Section: Introductionmentioning

confidence: 99%

Scenario Forecasting of Residential Load Profiles

Zhang

2020

IEEE J. Select. Areas Commun.

View full text Add to dashboard Cite

Load forecasting is an integral part of power system operations and planning. Due to the increasing penetration of rooftop PV, electric vehicles and demand response applications, forecasting the load of individual and a small group of households is becoming increasingly important. Forecasting the load accurately, however, is considerable more difficult when only a few households are included. A way to mitigate this challenge is to provide a set of scenarios instead of one point forecast, so an operator or utility can consider a range of behaviors. This paper proposes a novel scenario forecasting approach for residential load using flow-based conditional generative models. Compared to existing scenario forecasting methods, our approach can generate scenarios that are not only able to infer possible future realizations of residential load from the observed historical data but also realistic enough to cover a wide range of behaviors. Particularly, the flow-based models utilize a flow of reversible transformations to maximize the value of conditional density function of future load given the past observations. In order to better capture the complex temporal dependency of the forecasted future load on the condition, we extend the structure design for the reversible transformations in flow-based conditional generative models by strengthening the coupling between the output and the conditional input in the transformations. The simulation results show the flow-based designs outperform existing methods in scenario forecasting for residential load by both providing more accurate and more diverse scenarios. arXiv:1906.07373v1 [eess.SY]

show abstract

A scaling law for short term load forecasting on varying levels of aggregation

Cited by 87 publications

References 32 publications

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

Short-term Load Forecasting at Different Aggregation Levels with Predictability Analysis

Scenario Forecasting of Residential Load Profiles

Contact Info

Product

Resources

About