Machine Learning Based Short-Term Load Forecasting for Smart Meter Energy Consumption Data in London Households

2021 ◽  
Author(s):  
Doaa A. Bashawyah ◽  
Saeed Mian Qaisar
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Load Forecasting ◽  
Smart Meter ◽  
Short Term ◽  
Consumption Data ◽  
Short Term Load Forecasting

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.

Review of Machine Learning for Short Term Load Forecasting

2021 ◽  
pp. 180-190
Author(s):  
Aijia Ding ◽  
Huifen Chen ◽  
Tingzhang Liu
Keyword(s):  
Machine Learning ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting

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).

Machine learning based very short term load forecasting of machine tools

2020 ◽  
Vol 276 ◽  
pp. 115440 ◽  
Author(s):  
Bastian Dietrich ◽  
Jessica Walther ◽  
Matthias Weigold ◽  
Eberhard Abele
Keyword(s):  
Machine Learning ◽  
Machine Tools ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting

scholarly journals Short-Term Load Forecasting Based on the Transformer Model

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 516
Author(s):  
Zezheng Zhao ◽  
Chunqiu Xia ◽  
Lian Chi ◽  
Xiaomin Chang ◽  
Wei Li ◽  
...  
Keyword(s):  
Load Forecasting ◽  
Percentage Error ◽  
Short Term ◽  
Proposed Model ◽  
Consumption Data ◽  
Short Term Load Forecasting ◽  
Distribution Process ◽  
Set Up ◽  
Transformer Model ◽  
Similar Day

From the perspective of energy providers, accurate short-term load forecasting plays a significant role in the energy generation plan, efficient energy distribution process and electricity price strategy optimisation. However, it is hard to achieve a satisfactory result because the historical data is irregular, non-smooth, non-linear and noisy. To handle these challenges, in this work, we introduce a novel model based on the Transformer network to provide an accurate day-ahead load forecasting service. Our model contains a similar day selection approach involving the LightGBM and k-means algorithms. Compared to the traditional RNN-based model, our proposed model can avoid falling into the local minimum and outperforming the global search. To evaluate the performance of our proposed model, we set up a series of simulation experiments based on the energy consumption data in Australia. The performance of our model has an average MAPE (mean absolute percentage error) of 1.13, where RNN is 4.18, and LSTM is 1.93.

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