A new deep clustering method with application to customer selection for demand response program

Xiao, Jiang‐Wen; Xie, Yibing; Fang, Hongliang; Wang, Yan‐Wu

doi:10.1016/j.ijepes.2023.109072

Cited by 8 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite their short-term volatility, these renewable sources exhibit high predictability, primarily due to their strong correlation with weather patterns. For instance, [8] have prices and optimize power allocation, the market utilizes a market coupling algorithm known as EUPHEMIA. 4 Participants have the flexibility to adjust their positions within this market.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting Day-Ahead Electricity Prices in Irish Markets: Time Series and Machine Learning Approaches and Performance Evaluation

Mandadapu

2024

Preprint

View full text Add to dashboard Cite

Electricity price forecasting has become increasingly vital following the liberalization of electricity markets—ushering in a more competitive environment for electricity generation and distribution. Notably, electricity prices in Day-Ahead Markets (DAMs) hold significant sway, influencing decisions made by energy traders. However, modeling electricity prices poses challenges due to their inherent characteristics such as heteroscedasticity, sharp price spikes, and multiple levels of seasonality. Therefore, in this study, we delve into various methodologies from existing literature to forecast electricity spot prices within the Irish DAM. Our focus lies on employing time series and Machine Learning (ML) techniques to predict prices for all 24 hours of each DAM auction facilitated by the Single Electricity Market Operator (SEMO). For this, we begin by providing a concise overview of the electricity market and its functioning, particularly concerning our objective of price forecasting within the DAM. Subsequently, we elucidate the key aspects of the data utilized in this study. Following this, we offer succinct explanations of each model employed, detailing their structures and preparatory steps for the modeling task. Central to our analysis are the results showcasing the performance of each model relative to a benchmark, along with a brief discussion on the significance of predictors in the forecasting process. Finally, based on our findings, we draw conclusions and outline potential avenues for further research and development.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Markov regime-switching models are helpful for evaluating financial and economic time series because they allow for the probabilistic switching between regimes in data with discrete states 8. Jump diffusion models combine continuous diffusion processes with occasional jumps, capturing abrupt changes in asset prices or other phenomena in finance.…”

mentioning

confidence: 99%