scholarly journals Short-Term Load Forecasting Using Smart Meter Data: A Generalization Analysis

Processes ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 484
Author(s):  
Aida Mehdipour Pirbazari ◽  
Mina Farmanbar ◽  
Antorweep Chakravorty ◽  
Chunming Rong
Keyword(s):  
Energy Consumption ◽  
Peak Load ◽  
Load Forecasting ◽  
External Information ◽  
Smart Meter ◽  
Short Term ◽  
Efficient Operation ◽  
Ensemble Techniques ◽  
Short Term Load Forecasting ◽  
Continuous Power

Short-term load forecasting ensures the efficient operation of power systems besides affording continuous power supply for energy consumers. Smart meters that are capable of providing detailed information on buildings energy consumption, open several doors of opportunity to short-term load forecasting at the individual building level. In the current paper, four machine learning methods have been employed to forecast the daily peak and hourly energy consumption of domestic buildings. The utilized models depend merely on buildings historical energy consumption and are evaluated on the profiles that were not previously trained on. It is evident that developing data-driven models lacking external information such as weather and building data are of great importance under the situations that the access to such information is limited or the computational procedures are costly. Moreover, the performance evaluation of the models on separated house profiles determines their generalization ability for unseen consumption profiles. The conducted experiments on the smart meter data of several UK houses demonstrated that if the models are fed with sufficient historical data, they can be generalized to a satisfactory level and produce quite accurate results even if they only use past consumption values as the predictor variables. Furthermore, among the four applied models, the ones based on deep learning and ensemble techniques, display better performance in predicting daily peak load consumption than those of others.

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

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.

scholarly journals HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2672 ◽  
Author(s):  
Ivana Kiprijanovska ◽  
Simon Stankoski ◽  
Igor Ilievski ◽  
Slobodan Jovanovski ◽  
Matjaž Gams ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Error Score ◽  
Load Forecasting ◽  
Electrical Energy ◽  
Electrical Energy Consumption ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Short Term ◽  
Benchmark Model ◽  
Short Term Load Forecasting

Short-term load forecasting is integral to the energy planning sector. Various techniques have been employed to achieve effective operation of power systems and efficient market management. We present a scalable system for day-ahead household electrical energy consumption forecasting, named HousEEC. The proposed forecasting method is based on a deep residual neural network, and integrates multiple sources of information by extracting features from (i) contextual data (weather, calendar), and (ii) the historical load of the particular household and all households present in the dataset. Additionally, we compute novel domain-specific time-series features that allow the system to better model the pattern of energy consumption of the household. The experimental analysis and evaluation were performed on one of the most extensive datasets for household electrical energy consumption, Pecan Street, containing almost four years of data. Multiple test cases show that the proposed model provides accurate load forecasting results, achieving a root-mean-square error score of 0.44 kWh and mean absolute error score of 0.23 kWh, for short-term load forecasting for 300 households. The analysis showed that, for hourly forecasting, our model had 8% error (22 kWh), which is 4 percentage points better than the benchmark model. The daily analysis showed that our model had 2% error (131 kWh), which is significantly less compared to the benchmark model, with 6% error (360 kWh).

Comparative Study Of Short Term Load Forecasting Using Multilayer Feed Forward Neural Network With Back Propagation Learning And Radial Basis Functional Neural Network

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Amit Tiwari ◽  
Adarsh Dhar Dubey ◽  
And Devesh Patel
Keyword(s):  
Neural Network ◽  
Power System ◽  
Structural Changes ◽  
Unit Commitment ◽  
Network Reliability ◽  
Peak Load ◽  
Load Forecasting ◽  
Short Term ◽  
Feed Forward Neural Network ◽  
Short Term Load Forecasting

The term load forecast refers to the projected load requirement using systematic process of defining load in sufficient quantitative detail so that important power system expansion decisions can be made. Load forecasting is necessary for economic generation of power, economic allocation between plants (unit commitment scheduling), maintenance scheduling & for system security such as peak load shaving by power interchange with interconnected utilities. With structural changes to electricity in recent years, there is an emphasis on Short Term Load Forecasting (STLF).STLF is the essential part of power system planning & operation. Basic operating functions such as unit commitment, economic dispatch, and fuel scheduling & unit maintenance can be performed efficiently with an accurate forecast. Short term load forecasting can help to estimate load flows & to make decisions that can prevent overloading. Timely implementations of such decisions lead to improvement of network reliability & to the reduced occurrences of equipment failures & blackouts. The aim of short term load forecasting is to predict future electricity demands based, traditionally on historical data and predicted weather conditions. Short term load forecasting in its basic form is a statistical problem, where in the previous load values (time series variables) and influencing factors (casual variables) are used to determine the future loads.

scholarly journals Very short term load forecasting peak load time using fuzzy logic

2018 ◽  
Vol 403 ◽  
pp. 012070 ◽  
Author(s):  
J Jamaaluddin ◽  
D Hadidjaja ◽  
I Sulistiyowati ◽  
EA Suprayitno ◽  
I Anshory ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Peak Load ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting

Deep Ensemble Technique for Short-Term Load Forecasting Using Smart Meter Data

2021 ◽  
pp. 635-643
Author(s):  
A. L. Amutha ◽  
R. Annie Uthra ◽  
J. Preetha Roselyn ◽  
R. Golda Brunet
Keyword(s):  
Load Forecasting ◽  
Smart Meter ◽  
Short Term ◽  
Ensemble Technique ◽  
Short Term Load Forecasting

scholarly journals Very Short-Term Load Forecasting Using Hybrid Algebraic Prediction and Support Vector Regression

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Marlon Capuno ◽  
Jung-Su Kim ◽  
Hwachang Song
Keyword(s):  
Prediction Model ◽  
Support Vector Regression ◽  
Peak Load ◽  
Load Forecasting ◽  
Weather Forecast ◽  
Warning Signal ◽  
Support Vector ◽  
Short Term ◽  
Load Demand ◽  
Short Term Load Forecasting

This paper presents a model for very short-term load forecasting (VSTLF) based on algebraic prediction (AP) using a modified concept of the Hankel rank of a sequence. Moreover, AP is coupled with support vector regression (SVR) to accommodate weather forecast parameters for improved accuracy of a longer prediction horizon; thus, a hybrid model is also proposed. To increase system reliability during peak hours, this prediction model also aims to provide more accurate peak-loading conditions when considerable changes in temperature and humidity happen. The objective of going hybrid is to estimate an increase or decrease on the expected peak load demand by presenting the total MW per Celsius degree change (MW/C°) as criterion for providing a warning signal to system operators to prepare necessary storage facilities and sufficient reserve capacities if urgently needed by the system. The prediction model is applied using actual 2014 load demand of mainland South Korea during the summer months of July to September to demonstrate the performance of the proposed prediction model.

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