Advanced Statistical Learning on Short Term Load Process Forecasting
Short Term Load Forecast (STLF) is necessary for effective scheduling, operation optimization trading, and decision-making for electricity consumers. Modern and efficient machine learning methods are recalled nowadays to manage complicated structural big datasets, which are characterized by having a nonlinear temporal dependence structure. We propose different statistical nonlinear models to manage these challenges of hard type datasets and forecast 15-min frequency electricity load up to 2-days ahead. We show that the Long-short Term Memory (LSTM) and the Gated Recurrent Unit (GRU) models applied to the production line of a chemical production facility outperform several other predictive models in terms of out-of-sample forecasting accuracy by the Diebold-Mariano (DM) test with several metrics. The predictive information is fundamental for the risk and production management of electricity consumers.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingManagementSchedulingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Secure short-term load forecasting for smart grids with transformer-based federated learning
Electricity load forecasting is an essential task within smart grids to assist demand and supply balance. While advanced deep learning models require large amounts of high-resolution data for accurate short-term load pre…
Deep LearningFederated LearningLoad ForecastingFrom ARIMA to Attention: Power Load Forecasting Using Temporal Deep Learning
Accurate short-term power load forecasting is important to effectively manage, optimize, and ensure the robustness of modern power systems. This paper performs an empirical evaluation of a traditional statistical model a…
A Single Scalable LSTM Model for Short-Term Forecasting of Disaggregated Electricity Loads
Most electricity systems worldwide are deploying advanced metering infrastructures to collect relevant operational data. In particular, smart meters allow tracking electricity load consumption at a very disaggregated lev…
Time SeriesTime Series AnalysisArtificial Intelligence and Statistical Techniques in Short-Term Load Forecasting: A Review
Electrical utilities depend on short-term demand forecasting to proactively adjust production and distribution in anticipation of major variations. This systematic review analyzes 240 works published in scholarly journal…
Demand ForecastingLoad ForecastingES-dRNN with Dynamic Attention for Short-Term Load Forecasting
Short-term load forecasting (STLF) is a challenging problem due to the complex nature of the time series expressing multiple seasonality and varying variance. This paper proposes an extension of a hybrid forecasting mode…
Load ForecastingTime SeriesTime Series Analysis