Hybrid Deep Learning Gaussian Process for Deterministic and Probabilistic Load Forecasting

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

Download Full-text

Hybrid multitask multi-information fusion deep learning for household short-term load forecasting

IEEE Transactions on Smart Grid ◽

10.1109/tsg.2021.3091469 ◽

2021 ◽

pp. 1-1

Author(s):

Lianjie Jiang ◽

Xinli Wang ◽

Wei Li ◽

Lei Wang ◽

Xiaohong Yin ◽

...

Keyword(s):

Deep Learning ◽

Information Fusion ◽

Load Forecasting ◽

Short Term ◽

Short Term Load Forecasting

Download Full-text

A Novel Load Forecasting Approach Based on Smart Meter Data Using Advance Preprocessing and Hybrid Deep Learning

Applied Sciences ◽

10.3390/app11062742 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2742

Author(s):

Fatih Ünal ◽

Abdulaziz Almalaq ◽

Sami Ekici

Keyword(s):

Deep Learning ◽

Energy Consumption ◽

Prediction Models ◽

Short Term Memory ◽

Critical Role ◽

Load Forecasting ◽

Unified Framework ◽

Short Term ◽

Planning And Scheduling ◽

Short Term Load Forecasting

Short-term load forecasting models play a critical role in distribution companies in making effective decisions in their planning and scheduling for production and load balancing. Unlike aggregated load forecasting at the distribution level or substations, forecasting load profiles of many end-users at the customer-level, thanks to smart meters, is a complicated problem due to the high variability and uncertainty of load consumptions as well as customer privacy issues. In terms of customers’ short-term load forecasting, these models include a high level of nonlinearity between input data and output predictions, demanding more robustness, higher prediction accuracy, and generalizability. In this paper, we develop an advanced preprocessing technique coupled with a hybrid sequential learning-based energy forecasting model that employs a convolution neural network (CNN) and bidirectional long short-term memory (BLSTM) within a unified framework for accurate energy consumption prediction. The energy consumption outliers and feature clustering are extracted at the advanced preprocessing stage. The novel hybrid deep learning approach based on data features coding and decoding is implemented in the prediction stage. The proposed approach is tested and validated using real-world datasets in Turkey, and the results outperformed the traditional prediction models compared in this paper.

Download Full-text

Non-parametric probabilistic load flow using Gaussian process learning

Physica D Nonlinear Phenomena ◽

10.1016/j.physd.2021.132941 ◽

2021 ◽

pp. 132941

Author(s):

Parikshit Pareek ◽

Chuan Wang ◽

Hung D. Nguyen

Keyword(s):

Gaussian Process ◽

Load Flow ◽

Probabilistic Load Flow ◽

Process Learning ◽

Probabilistic Load ◽

Non Parametric

Download Full-text

Probabilistic Load Forecasting Optimization for Building Energy Models via Day Characterization

Sensors ◽

10.3390/s21093299 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3299

Author(s):

Eva Lucas Segarra ◽

Germán Ramos Ruiz ◽

Carlos Fernández Bandera

Keyword(s):

Optimization Technique ◽

Load Forecasting ◽

Weather Forecast ◽

Building Energy ◽

Load Forecast ◽

Energy Saving Potential ◽

Energy Models ◽

Building Monitoring ◽

Probabilistic Load ◽

Grid Operators

Accurate load forecasting in buildings plays an important role for grid operators, demand response aggregators, building energy managers, owners, customers, etc. Probabilistic load forecasting (PLF) becomes essential to understand and manage the building’s energy-saving potential. This research explains a methodology to optimize the results of a PLF using a daily characterization of the load forecast. The load forecast provided by a calibrated white-box model and a real weather forecast was classified and hierarchically selected to perform a kernel density estimation (KDE) using only similar days from the database characterized quantitatively and qualitatively. A real case study is presented to show the methodology using an office building located in Pamplona, Spain. The building monitoring, both inside—thermal sensors—and outside—weather station—is key when implementing this PLF optimization technique. The results showed that thanks to this daily characterization, it is possible to optimize the accuracy of the probabilistic load forecasting, reaching values close to 100% in some cases. In addition, the methodology explained is scalable and can be used in the initial stages of its implementation, improving the values obtained daily as the database increases with the information of each new day.

Download Full-text