Short Term Load Forecasting

Abstract

Electrification of transport and heating, and the integration of low carbon technologies (LCT) is driving the need to know when and how much electricity is being consumed and generated by consumers. It is also important to know what external factors influence individual electricity demand. Low voltage networks connect the end users through feeders and substations, and thus encompass diverse strata of society. Some feeders may be small with only a handful of households, while others may have over a hundred customers. Some low voltage networks include small-to-medium enterprises (SMES), or hospitals and schools, but others may be entirely residential. Furthermore, local feeders will also likely register usage from lighting in common areas of apartments or flats, street lighting and other street furniture such as traffic lights. Moreover, the way that different households on the same feeder or substation use electricity may be drastically different. For example, load profiles of residential households will vary significantly depending on the size of their houses, occupancy, socio-demographic characteristics and lifestyle. Profiles will also depend on whether households have solar panels, overnight storage heating (OSH) or electric vehicles [1]. Thus, knowing how and when people use electricity in their homes and communities is a fundamental part of understanding how to effectively generate and distribute electrical energy. In short term load forecasting, the aim is to estimate the load for the next half hour up to the next two weeks. For aggregated household demand, many different methods are proposed and tested (see e.g. Alfares and Nazeeruddin [2], Taylor and Espasa [3], Hong and Fan [4], etc.). Aggregating the data smooths it, therefore makes it easier to forecast. The individual level demand forecasting is more challenging and comes with higher errors, as shown in Singh et al. [5], Haben et al. [1]. The growth of literature on short term load forecasting at the individual level has started with the wider access to higher resolution data in the last two decades, and is still developing.

Additive Ensemble Neural Network with Constrained Weighted Quantile Loss for Probabilistic Electric-Load Forecasting

“…The best average result for the different test iterations is obtained for the neural network (NN) with an RMSE of 2.48. The work in [ 49 ] presents a theoretical review of the most commonly used ML methods for short-term load forecast (STLF), including NN and support vector for regression. Time-series statistical analysis models for SMTLF are discussed in detail in [ 4 ] with forecasts at an hour interval applied to load data from the Electric Reliability Council of Texas (ERCOT).…”

Real-Time Operation of Microgrids

“…Controllable loads consist of load profiles that are generally time varying and are mainly driven by the type of customer behavior. Methods such as linear regression, time series, autoregressive, exponential smoothing, curve fitting, permutation and machine learning, are used for load forecasting [15]. Using curve fitting and given the dominant load profile at MG site under study, a piecewise function in three-time intervals is developed and gives the forecasted total power demand ( ) D P t at time t as follows:…”

“…are represented in discrete time with a step size T. Two factors impact the simulation of RTS, namely: the model complexity and the computational speed of the installed hardware, [15]. Reactive power coordination control process was used in [21] to stabilize a 50 MWp PV power planet connected directly to the utility grid.…”

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

“…VSTLF helps to calculate the electric loads of half-hour or a few minutes [35]. The purpose of the STLF is to measure the load from one hour to the next few weeks [36]. MTLF predicts the load from one month to one or more years [37].…”