Experiment on Electricity Consumption Prediction using Long Short-Term Memory Architecture on Residential Electrical Consumer

2021 ◽  
Author(s):  
Nur Shakirah Md Salleh ◽  
Azizah Suliman ◽  
Bo Norregaard Jorgensen
Keyword(s):  
Short Term Memory ◽  
Electricity Consumption ◽  
Memory Architecture ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Consumption Prediction

scholarly journals Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

2020 ◽  
Vol 13 (1) ◽  
pp. 104
Author(s):  
Dana-Mihaela Petroșanu ◽  
Alexandru Pîrjan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Short Term Memory ◽  
Electricity Consumption ◽  
Short Term ◽  
Term Memory ◽  
Storage Room ◽  
Long Short Term Memory ◽  
Commercial Center ◽  
Business And Management

The accurate forecasting of the hourly month-ahead electricity consumption represents a very important aspect for non-household electricity consumers and system operators, and at the same time represents a key factor in what regards energy efficiency and achieving sustainable economic, business, and management operations. In this context, we have devised, developed, and validated within the paper an hourly month ahead electricity consumption forecasting method. This method is based on a bidirectional long-short-term memory (BiLSTM) artificial neural network (ANN) enhanced with a multiple simultaneously decreasing delays approach coupled with function fitting neural networks (FITNETs). The developed method targets the hourly month-ahead total electricity consumption at the level of a commercial center-type consumer and for the hourly month ahead consumption of its refrigerator storage room. The developed approach offers excellent forecasting results, highlighted by the validation stage’s results along with the registered performance metrics, namely 0.0495 for the root mean square error (RMSE) performance metric for the total hourly month-ahead electricity consumption and 0.0284 for the refrigerator storage room. We aimed for and managed to attain an hourly month-ahead consumed electricity prediction without experiencing a significant drop in the forecasting accuracy that usually tends to occur after the first two weeks, therefore achieving a reliable method that satisfies the contractor’s needs, being able to enhance his/her activity from the economic, business, and management perspectives. Even if the devised, developed, and validated forecasting solution for the hourly consumption targets a commercial center-type consumer, based on its accuracy, this solution can also represent a useful tool for other non-household electricity consumers due to its generalization capability.

scholarly journals Long Short-Term Memory Network-Based Metaheuristic for Effective Electric Energy Consumption Prediction

2021 ◽  
Vol 11 (23) ◽  
pp. 11263
Author(s):  
Simran Kaur Hora ◽  
Rachana Poongodan ◽  
Rocío Pérez de Prado ◽  
Marcin Wozniak ◽  
Parameshachari Bidare Divakarachari
Keyword(s):  
Energy Management ◽  
Short Term Memory ◽  
Electric Energy ◽  
Short Term ◽  
Term Memory ◽  
Electric Energy Consumption ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

The Electric Energy Consumption Prediction (EECP) is a complex and important process in an intelligent energy management system and its importance has been increasing rapidly due to technological developments and human population growth. A reliable and accurate model for EECP is considered a key factor for an appropriate energy management policy. In recent periods, many artificial intelligence-based models have been developed to perform different simulation functions, engineering techniques, and optimal energy forecasting in order to predict future energy demands on the basis of historical data. In this article, a new metaheuristic based on a Long Short-Term Memory (LSTM) network model is proposed for an effective EECP. After collecting data sequences from the Individual Household Electric Power Consumption (IHEPC) dataset and Appliances Load Prediction (AEP) dataset, data refinement is accomplished using min-max and standard transformation methods. Then, the LSTM network with Butterfly Optimization Algorithm (BOA) is developed for EECP. In this article, the BOA is used to select optimal hyperparametric values which precisely describe the EEC patterns and discover the time series dynamics in the energy domain. This extensive experiment conducted on the IHEPC and AEP datasets shows that the proposed model obtains a minimum error rate relative to the existing models.

scholarly journals Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 605
Author(s):  
Ijaz Ul Haq ◽  
Amin Ullah ◽  
Samee Ullah Khan ◽  
Noman Khan ◽  
Mi Young Lee ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Short Term Memory ◽  
Electrical Energy ◽  
Sequential Learning ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

The use of electrical energy is directly proportional to the increase in global population, both concerning growing industrialization and rising residential demand. The need to achieve a balance between electrical energy production and consumption inspires researchers to develop forecasting models for optimal and economical energy use. Mostly, the residential and industrial sectors use metering sensors that only measure the consumed energy but are unable to manage electricity. In this paper, we present a comparative analysis of a variety of deep features with several sequential learning models to select the optimized hybrid architecture for energy consumption prediction. The best results are achieved using convolutional long short-term memory (ConvLSTM) integrated with bidirectional long short-term memory (BiLSTM). The ConvLSTM initially extracts features from the input data to produce encoded sequences that are decoded by BiLSTM and then proceeds with a final dense layer for energy consumption prediction. The overall framework consists of preprocessing raw data, extracting features, training the sequential model, and then evaluating it. The proposed energy consumption prediction model outperforms existing models over publicly available datasets, including Household and Korean commercial building datasets.

scholarly journals A short-term hybrid forecasting model for time series electrical-load data using random forest and bidirectional long short-term memory

2021 ◽  
Vol 11 (1) ◽  
pp. 763
Author(s):  
Zannatul Ferdoush ◽  
Booshra Nazifa Mahmud ◽  
Amitabha Chakrabarty ◽  
Jia Uddin
Keyword(s):  
Random Forest ◽  
Hybrid Model ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Electricity Consumption ◽  
Infrastructure Development ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

In the presence of the deregulated electric industry, load forecasting is more demanded than ever to ensure the execution of applications such as energy generation, pricing decisions, resource procurement, and infrastructure development. This paper presents a hybrid machine learning model for short-term load forecasting (STLF) by applying random forest and bidirectional long short-term memory to acquire the benefits of both methods. In the experimental evaluation, we used a Bangladeshi electricity consumption dataset of 36 months. The paper provides a comparative study between the proposed hybrid model and state-of-art models using performance metrics, loss analysis, and prediction plotting. Empirical results demonstrate that the hybrid model shows better performance than the standard long short-term memory and the bidirectional long short-term memory models by exhibiting more accurate forecast results.

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