Improving the Model for Energy Consumption Load Demand Forecasting

2012 ◽  
Vol 132 (3) ◽  
pp. 235-243
Author(s):  
Pituk Bunnoon ◽  
Kusumal Chalermyanont ◽  
Chusak Limsakul
Keyword(s):  
Energy Consumption ◽  
Demand Forecasting ◽  
Load Demand

Estimating Household Travel Energy Consumption in Conjunction with a Travel Demand Forecasting Model

2017 ◽  
Vol 2668 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Venu M. Garikapati ◽  
Daehyun You ◽  
Wenwen Zhang ◽  
Ram M. Pendyala ◽  
Subhrajit Guhathakurta ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Urban Form ◽  
Travel Demand ◽  
Demand Forecasting ◽  
The United States ◽  
Metropolitan Region ◽  
Demand Model ◽  
Awareness Campaigns ◽  
Energy Footprint ◽  
Household Travel

This paper presents a methodology for the calculation of the consumption of household travel energy at the level of the traffic analysis zone (TAZ) in conjunction with information that is readily available from a standard four-step travel demand model system. This methodology embeds two algorithms. The first provides a means of allocating non-home-based trips to residential zones that are the source of such trips, whereas the second provides a mechanism for incorporating the effects of household vehicle fleet composition on fuel consumption. The methodology is applied to the greater Atlanta, Georgia, metropolitan region in the United States and is found to offer a robust mechanism for calculating the footprint of household travel energy at the level of the individual TAZ; this mechanism makes possible the study of variations in the energy footprint across space. The travel energy footprint is strongly correlated with the density of the built environment, although socioeconomic differences across TAZs also likely contribute to differences in travel energy footprints. The TAZ-level calculator of the footprint of household travel energy can be used to analyze alternative futures and relate differences in the energy footprint to differences in a number of contributing factors and thus enables the design of urban form, formulation of policy interventions, and implementation of awareness campaigns that may produce more-sustainable patterns of energy consumption.

scholarly journals Ultra-Short-Term Load Demand Forecast Model Framework Based on Deep Learning

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4900 ◽  
Author(s):  
Hongze Li ◽  
Hongyu Liu ◽  
Hongyan Ji ◽  
Shiying Zhang ◽  
Pengfei Li
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Meteorological Data ◽  
Demand Forecasting ◽  
Dense Layer ◽  
Load Prediction ◽  
Demand Forecast ◽  
Short Term ◽  
Model Framework ◽  
Load Demand

Ultra-short-term load demand forecasting is significant to the rapid response and real-time dispatching of the power demand side. Considering too many random factors that affect the load, this paper combines convolution, long short-term memory (LSTM), and gated recurrent unit (GRU) algorithms to propose an ultra-short-term load forecasting model based on deep learning. Firstly, more than 100,000 pieces of historical load and meteorological data from Beijing in the three years from 2016 to 2018 were collected, and the meteorological data were divided into 18 types considering the actual meteorological characteristics of Beijing. Secondly, after the standardized processing of the time-series samples, the convolution filter was used to extract the features of the high-order samples to reduce the number of training parameters. On this basis, the LSTM layer and GRU layer were used for modeling based on time series. A dropout layer was introduced after each layer to reduce the risk of overfitting. Finally, load prediction results were output as a dense layer. In the model training process, the mean square error (MSE) was used as the objective optimization function to train the deep learning model and find the optimal super parameter. In addition, based on the average training time, training error, and prediction error, this paper verifies the effectiveness and practicability of the load prediction model proposed under the deep learning structure in this paper by comparing it with four other models including GRU, LSTM, Conv-GRU, and Conv-LSTM.

scholarly journals A Secure Federated Deep Learning-Based Approach for Heating Load Demand Forecasting in Building Environment

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Arash Moradzadeh ◽  
Hamed Moayyed ◽  
Behnam Mohammadi-Ivatloo ◽  
A Pedro Aguiar ◽  
Amjad Anvari-Moghaddam
Keyword(s):  
Deep Learning ◽  
Demand Forecasting ◽  
Load Demand ◽  
Heating Load ◽  
Building Environment

scholarly journals PRONÓSTICO DE DEMANDA DE ENERGÍA ELÉCTRICA USANDO PROCESOS GAUSSIANOS: UN ANÁLISIS COMPARATIVO

2021 ◽  
Vol 5 ◽  
pp. 85-99
Author(s):  
Jorge Eliecer Loaiza Muñoz ◽  
Carlos D. Zuluaga
Keyword(s):  
Power Systems ◽  
Gaussian Processes ◽  
Energy Demand ◽  
Demand Forecasting ◽  
Real Data ◽  
Point Of View ◽  
Covariance Functions ◽  
Load Demand ◽  
Invaluable Tool ◽  
Grid Operators

Abstract—Load demand forecasting is an essential component for planning power systems, and it is an invaluable tool to grid operators or customers. Many methods have been proposed to provide reliable estimates of electric load demand, but few methods can address the problem of predicting energy demand from a probabilistic point of view. One of them is the Gaussian processes (GP) that considering an adequate covariance function are suitable tools to carry out this load forecasting task. In this article, we show how to use Gaussian processes to predict elec- trical energy demand. Additionally, we thoroughly test various covariance functions and provide a new one. The performance of the proposed methodology was tested on two real data sets, showing that GPs are competitive alternatives for short-term load demand forecasting compared to other state-of-the-art methods

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