Short-Term Load Forecasting Based on a Semi-Parametric Additive Model
By Shu Fan, Rob J Hyndman. IEEE Transactions on Power Systems, 2012
496 citations recorded by the source corpus.
Short-term load forecasting is an essential instrument in power system planning, operation, and control. Many operating decisions are based on load forecasts, such as dispatch scheduling of generating capacity, reliability analysis, and maintenance planning for the generators. Overestimation of electricity demand will cause a conservative operation, which leads to the start-up of too many units or excessive energy purchase, thereby supplying an unnecessary level of reserve. On the other hand, underestimation may result in a risky operation, with insufficient preparation of spinning reserve, causing the system to operate in a vulnerable region to the disturbance. In this paper, semi-parametric additive models are proposed to estimate the relationships between demand and the driver variables. Specifically, the inputs for these models are calendar variables, lagged actual demand observations, and historical and forecast temperature traces for one or more sites in the target power system. In addition to point forecasts, prediction intervals are also estimated using a modified bootstrap method suitable for the complex seasonality seen in electricity demand data. The proposed methodology has been used to forecast the half-hourly electricity demand for up to seven days ahead for power systems in the Australian National Electricity Market. The performance of the methodology is validated via out-of-sample experiments with real data from the power system, as well as through on-site implementation by the system operator.
Subfields: Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/ca9d7976e1683aa2acfae757b2b2ef4cf0b06999
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Citations in the corpus
- Probabilistic Load Forecasting via Quantile Regression Averaging on Sister Forecasts (2017, methodology): “value of future load using various techniques, primarily statistical techniques (such as regression models, exponential smoothing and time series models), and artificial intelligence techniques (such as neural networks and support vector machines) [3]-[7].”
- Long Term Probabilistic Load Forecasting and Normalization With Hourly Information (2014, background): “Most literature in the load forecasting field has been devoted to short term load forecasting, of which the forecasting horizon is two weeks or less [1-7].”
- Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals (2014, methodology): “In this paper, the inputs of NN are chosen based on the ACF and PACF analyses, as well as some empirical guidelines from [39]–[42].”
- A gradient boosting approach to the Kaggle load forecasting competition (2014, methodology): “Our approach has been influenced by the work of Fan and Hyndman (2012) who developed semi-parametric additive models with penalised spline functions for forecasting electricity demand in various Australian states.”
- Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges (2018, methodology): “additive model was proposed in [105] to forecast the load of Australian National Electricity Market.”
- Functional time series approach for forecasting very short-term electricity demand (2013, background): “Because the MAPE captures the proportionality between the forecast error and actual demand, it has become an industry standard forecast error measure in demand forecasting [12,41].”
- Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities (2015, methodology): “For example, Fan and Hyndman [11] presented semi-parametric additive models to estimate the relationships between demand and the driver variables such as calendar variables, lagged actual demand observations, and historical and forecast temperature traces.”
- A refined parametric model for short term load forecasting (2014, methodology): “Another alternative to MWS is used in Fan & Hyndman (2012).”
- Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem (2018, methodology): “2 An overview of the literature on load forecasting Short term load forecasting (STLF) has been receiving a lot of attention [10].”