Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load

By Lei Xu, Shengwei Wang, R. Tang. Applied Energy, 2019

147 citations recorded by the source corpus.

Abstract This paper presents a new probabilistic building load forecasting model considering uncertainties in weather forecasts and abnormal peak load. Two basis function components are developed, including the probabilistic normal load forecasting and the probabilistic uncertain peak load (or abnormal peak load) forecasting. The probabilistic normal load forecasting model is built using the artificial neural network (ANN) and the probabilistic temperature forecasts. The probabilistic abnormal peak load forecasting model consists of two models quantifying the probabilistic occurrence and magnitude of the peak abnormal differential load respectively. The test results show that the ANN deterministic load forecasting model can achieve satisfactory performance with the average mean absolute percentage error (MAPE) of 5.0%. The probabilistic occurrence model can forecast the occurrence frequency of the peak abnormal differential load with the satisfactory agreement, and the probabilistic magnitude model can well forecast the magnitudes of the peak abnormal differential load with the Kolmogorov-Smirnov error of 0.09. Real-time application case studies are conducted by different means of using the probabilistic weather forecasts. The results show that the probabilistic normal load forecasts have satisfactory accuracies and the load forecasts based on the one-day-ahead probabilistic weather forecasts are the best.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/4ccb8f7ca74229fb03783afb8931738dea05cee3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Benjamin C. M. Fung
2. Ying Sun
3. F. Haghighat

In Electrical and Electronic Engineering

1. Benjamin C. M. Fung
2. Ying Sun
3. F. Haghighat

In Management Science and Operations Research

1. Benjamin C. M. Fung
2. Philip S. Yu

Citations in the corpus

• A review of the-state-of-the-art in data-driven approaches for building energy prediction (2020, background): “[115] 21924 3132 - 1 Meteorological information, time index ANN Educational Electricity power demand Building 1hour 24hour, 9days 24, 216 MAE, MAPE, RMSE”