High frequency short-term demand forecasting model for distribution power grid based on ARIMA

2012 ◽  
Author(s):  
Hongming He ◽  
Tao Liu ◽  
Ruimin Chen ◽  
Yong Xiao ◽  
Jinfeng Yang
Keyword(s):  
High Frequency ◽  
Power Grid ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Short Term ◽  
Demand Forecasting Model ◽  
Grid Based

Stream-Based Short-Term Demand Forecasting Model Using ARIMA

2012 ◽  
Vol 220-223 ◽  
pp. 315-318
Author(s):  
Tao Liu ◽  
Rui Min Chen ◽  
Yong Xiao ◽  
Jin Feng Yang
Keyword(s):  
High Speed ◽  
Power Grid ◽  
Demand Forecasting ◽  
Streaming Data ◽  
Forecasting Model ◽  
Short Term ◽  
Smart Meters ◽  
Short Term Load Forecasting ◽  
Demand Forecasting Model ◽  
And Control

Short-term load forecasting for streaming load data is an important issue for power system planning, operation and control. Smart meters of Advanced Meter Infrastructure distributed around the distribution power grid produce streams of load at high-speed. The collected data can be characterized as a non-stationary continuous flow. A stream-based short-term demand forecasting model based on ARIMA is proposed. This method is used to forecast hourly electricity demand for next few days ahead. The performance of this methodology is validated with streaming data collected in real-time from the power grid.

scholarly journals A Probabilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain

Water ◽  
2017 ◽  
Vol 9 (7) ◽  
pp. 507 ◽  
Author(s):  
Francesca Gagliardi ◽  
Stefano Alvisi ◽  
Zoran Kapelan ◽  
Marco Franchini
Keyword(s):  
Markov Chain ◽  
Water Demand ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Short Term ◽  
Water Demand Forecasting ◽  
Model Based ◽  
Demand Forecasting Model

scholarly journals A short-term, pattern-based model for water-demand forecasting

2007 ◽  
Vol 9 (1) ◽  
pp. 39-50 ◽  
Author(s):  
Stefano Alvisi ◽  
Marco Franchini ◽  
Alberto Marinelli
Keyword(s):  
Water Distribution ◽  
Demand Forecasting ◽  
Distribution Networks ◽  
Fine Tuning ◽  
Forecasting Model ◽  
Periodic Patterns ◽  
Short Term ◽  
Feed Forward Control ◽  
Demand Forecasting Model ◽  
Future Demands

The short-term, demand-forecasting model described in this paper forms the third constituent part of the POWADIMA research project which, taken together, address the issue of real-time, near-optimal control of water-distribution networks. Since the intention is to treat water distribution as a feed-forward control system, operational decisions have to be based on the expected future demands for water, rather than just the present known requirements. Accordingly, it was necessary to develop a short-term, demand-forecasting procedure. To that end, monitoring facilities were installed to measure short-term fluctuations in demands for a small experimental network, which enabled a thorough investigation of trends and periodicities that can usually be found in this type of time-series. On the basis of these data, a short-term, demand-forecasting model was formulated. The model reproduces the periodic patterns observed at annual, weekly and daily levels prior to fine-tuning the estimated values of future demands through the inclusion of persistence effects. Having validated the model, the demand forecasts were subjected to an analysis of the sensitivity to possible errors in the various components of the model. Its application to much larger case studies is described in the following two papers.

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

scholarly journals Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3204
Author(s):  
Michał Sabat ◽  
Dariusz Baczyński
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Electric Energy ◽  
Forecasting Model ◽  
Short Term ◽  
Complex Evaluation ◽  
Proposed Model ◽  
Energy Demand Forecasting ◽  
Pareto Fronts ◽  
Naive Approach

Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This study depicted an attempt to develop an appropriate method by introducing a novel forecasting model based on the idea to use the Pareto fronts as a tool to select data in the forecasting process. The proposed model was implemented to forecast short-term electric energy demand in Poland using historical hourly demand values from Polish TSO. The study rather intended on implementing the range of different approaches—scenarios of Pareto fronts usage than on a complex evaluation of the obtained results. However, performance of proposed models was compared with a few benchmark forecasting models, including naïve approach, SARIMAX, kNN, and regression. For two scenarios, it has outperformed all other models by minimum 7.7%.

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