Forecasting Short-Term Electric Load with a Hybrid of ARIMA Model and LSTM Network

Pooniwala, Nevil; Sutar, Rajendra G.

doi:10.1109/iccci50826.2021.9402461

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…ARIMA is an extended approach to moving average, which tries to transform non-stationary time-series into stationary ones and considers their previous values to predict the next ones. It is commonly used in recent studies about electric load prediction [64,65].…”

Section: Energy Consumption Forecasting Methodsmentioning

confidence: 99%

Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions

et al. 2022

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Industry 4.0 and its technologies allow advancements in communications, production and management efficiency across several segments. In smart grids, essential parts of smart cities, smart meters act as IoT devices that can gather data and help the management of the sustainable energy matrix, a challenge that is faced worldwide. This work aims to use smart meter data and household features data to seek the most appropriate methods of energy consumption prediction. Using the Cross-Industry Standard Process for Data Mining (CRISP-DM) method, Python Platform, and several prediction methods, prediction experiments were performed with household feature data and past consumption data of over 470 smart meters that gathered data for three years. Support vector machines, random forest regression, and neural networks were the best prediction methods among the ones tested in the sample. The results help utilities (companies that maintain the infrastructure for public services) to offer better contracts to new households and to manage their smart grid infrastructure based on the forecasted demand.

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Section: Energy Consumption Forecasting Methodsmentioning

confidence: 99%

Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions

et al. 2022

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“…In [19], an exponential smoothing state-space model and an artificial neural network are combined. The system from [20] combines a SARIMAX with a long-short term memory network to obtain a better forecasting performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Selection of Temperature Variables for Short-Term Load Forecasting

et al. 2022

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Due to the infeasibility of large-scale electrical energy storage, electricity is generated and consumed simultaneously. Therefore, electricity entities need consumption forecasting systems to plan operations and manage supplies. In addition, accurate predictions allow renewable energies on electrical grids to be managed, thereby reducing greenhouse gas emissions. Temperature affects electricity consumption through air conditioning and heating equipment, although it is the consumer’s behavior that determines specifically to what extent. This work proposes an automatic method of processing and selecting variables, with a two-fold objective: improving both the accuracy and the interpretability of the overall forecasting system. The procedure has been tested by the predictive system of the Spanish electricity operator (Red Eléctrica de España) with regard to peninsular demand. During the test period, the forecasting error was consistently reduced for the forecasting horizon, with an improvement of 0.16% in MAPE and 59.71 MWh in RMSE. The new way of working with temperatures is interpretable, since they separate the effect of temperature according to location and time. It has been observed that heat has a greater influence than the cold. In addition, on hot days, the temperature of the second previous day has a greater influence than the previous one, while the opposite occurs on cold days.

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“…Time series forecasting techniques are divided into two parts. First, statistical mathematical models such as moving averages, exponential smoothing, regression and ARIMA [8], [9]. Second, artificial intelligence models such as neural networks, genetic algorithms, simulated annealing, and generic programming.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Forecasting Daily Electricity Loads Using Seasonal Arima Patterns of Generation Units at Pt. PLN (Persero) Tarakan City

Mado,

Budiman,

Triwiyatno

2023

kursor

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Electrical power requirements at load centers tend to change over time, so the State Electricity Company (PLN) as a provider of electrical energy must be able to predict electrical load requirements every day. The city of Tarakan as a reference center in the northern region of Indonesia is developing rapidly. Along with this growth, the need for electric power is of course also increasing, so we must be able to provide an economical and reliable electric power supply system. This research aims to predict the electricity load at PT. PLN (Persero) Tarakan City. The author will carry out short-term forecasting using time series data in the form of daily electrical power usage data using the Autoregressive Integrated Moving Average (ARIMA) method. The ARIMA method or often called the Box-Jenkins technique shows that this method is suitable for predicting a number of variables quickly, simply and cheaply because it only requires variable data to be predicted. Analysis based on the Box-Jenkins time series taking into account the influence of seasonal patterns. The prediction results show that the data contains seasonal elements with the best model being SARIMA with a MAPE of 3 percent.

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Forecasting Short-Term Electric Load with a Hybrid of ARIMA Model and LSTM Network

Cited by 8 publications

References 13 publications

Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions

Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions

Automatic Selection of Temperature Variables for Short-Term Load Forecasting

Short-Term Forecasting Daily Electricity Loads Using Seasonal Arima Patterns of Generation Units at Pt. PLN (Persero) Tarakan City

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