Abstract
To achieve high accuracy in prediction, a load forecasting algorithm must model various consumer behaviors in response to weather conditions or special events. Different triggers will have various effects on different customers and lead to difficulties in constructing an adequate prediction model due to non-stationary and uncertain characteristics in load variations. This paper proposes an open-ended model of short-term load forecasting (STLF) which has general prediction ability to capture the non-linear relationship between the load demand and the exogenous inputs. The prediction method uses the whale optimization algorithm, discrete wavelet transform, and multiple linear regression model (WOA-DWT-MLR model) to predict both system load and aggregated load of power consumers. WOA is used to optimize the best combination of detail and approximation signals from DWT to construct an optimal MLR model. The proposed model is validated with both the system-side data set and the end-user data set for Independent System Operator-New England (ISO-NE) and smart meter load data, respectively, based on Mean Absolute Percentage Error (MAPE) criterion. The results demonstrate that the proposed method achieves lower prediction error than existing methods and can have consistent prediction of non-stationary load conditions that exist in both test systems. The proposed method is, thus, beneficial to use in the energy management system.
Highlights
Necessity to construct a short-term load forecasting (STLF) model that has flexible applications at the system’s level and end user’s level grows rapidly by the common implementation of a future ahead bidding system, such as day-ahead demand response programs
The decomposition level of discrete wavelet transform (DWT) tuned by whale optimization algorithm (WOA) is between level-2 and level-5, while the type of DWT is between Db1 to Db5
The prediction results of traditional multiple linear regression (MLR), artificial neural network (ANN) [25], autoregressive moving average with exogenous input (ARMAX) [26], support vector regression (SVR) [27,28], and particle swarm optimization (PSO)-DWT-MLR are provided to show the effectiveness of proposed WOA-DWT-MLR
Summary
Necessity to construct a short-term load forecasting (STLF) model that has flexible applications at the system’s level and end user’s level grows rapidly by the common implementation of a future ahead bidding system, such as day-ahead demand response programs. To construct a closely fitting model of the load, a ubiquitous implementation of DWT is strongly related to proper selection of both the decomposition level and type of wavelet. The proposed model provides a more accurate prediction method that closely fits system-side loads and aggregated loads at downstream level (end-user). The signal is independently decomposed to the set of Daubechies levels and types by handling the selected decomposition coefficients, while the remaining coefficients are replaced with zero Based on this scheme, the signal can be extracted with its unique characteristics reflected by individual approximation and detail components. Accurate and consistent implementation of STLF is achieved in both system-side and aggregated end-user data sets by integrating WOA-based DWT in the MLR model.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
