Medium-Term Regional Electricity Load Forecasting through Machine Learning and Deep Learning

Shirzadi, Navid; Nizami, Ameer; Khazen, Mohammadali; Nik-Bakht, Mazdak

doi:10.3390/designs5020027

Cited by 39 publications

(22 citation statements)

References 37 publications

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“…There have been a few studies where the traditional machine learning algorithm and deep learning algorithms were compared in terms of their forecasting accuracies. For example, Paterakis et al ( 2017 ) compared multiple layer perceptron against Random forest, SVM, and other regressors; Ağbulut ( 2022 ) compared deep neural networks against SVM to predict the energy demands for the transport sector; Bakay and Ağbulut ( 2021 ) compared deep neural networks against SVM and ANN to forecast electricity demands in Turkey; Shirzadi et al ( 2021 ) compared LSTMs against SVM and random forest to forecast long-term power demands in Ontario, Canada. In all these examples, the deep learning algorithms outperformed the basic machine learning algorithms.…”

Section: Related Workmentioning

confidence: 99%

Incorporating causality in energy consumption forecasting using deep neural networks

2022

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Forecasting energy demand has been a critical process in various decision support systems regarding consumption planning, distribution strategies, and energy policies. Traditionally, forecasting energy consumption or demand methods included trend analyses, regression, and auto-regression. With advancements in machine learning methods, algorithms such as support vector machines, artificial neural networks, and random forests became prevalent. In recent times, with an unprecedented improvement in computing capabilities, deep learning algorithms are increasingly used to forecast energy consumption/demand. In this contribution, a relatively novel approach is employed to use long-term memory. Weather data was used to forecast the energy consumption from three datasets, with an additional piece of information in the deep learning architecture. This additional information carries the causal relationships between the weather indicators and energy consumption. This architecture with the causal information is termed as entangled long short term memory. The results show that the entangled long short term memory outperforms the state-of-the-art deep learning architecture (bidirectional long short term memory). The theoretical and practical implications of these results are discussed in terms of decision-making and energy management systems.

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Section: Related Workmentioning

confidence: 99%

Incorporating causality in energy consumption forecasting using deep neural networks

2022

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“…ML algorithms have enhanced the performance of STLF by showing profound accuracy in dealing with non-linearities of the electrical load data and accurate forecasting of the peaks of electrical load as compared to the statistical regression and dimension reduction models [23][24][25]. ML methods mainly comprise Artificial Neural Networks (ANNs) which can handle the stochastic nature of the weather-sensitive loads during the prediction of electrical load forecasting [26][27][28]. The conventional ANN algorithms experience overfitting problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

et al. 2021

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Power system planning in numerous electric utilities merely relies on the conventional statistical methodologies, such as ARIMA for short-term electrical load forecasting, which is incapable of determining the non-linearities induced by the non-linear seasonal data, which affect the electrical load. This research work presents a comprehensive overview of modern linear and non-linear parametric modeling techniques for short-term electrical load forecasting to ensure stable and reliable power system operations by mitigating non-linearities in electrical load data. Based on the findings of exploratory data analysis, the temporal and climatic factors are identified as the potential input features in these modeling techniques. The real-time electrical load and meteorological data of the city of Lahore in Pakistan are considered to analyze the reliability of different state-of-the-art linear and non-linear parametric methodologies. Based on performance indices, such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), the qualitative and quantitative comparisons have been conferred among these scientific rationales. The experimental results reveal that the ANN–LM with a single hidden layer performs relatively better in terms of performance indices compared to OE, ARX, ARMAX, SVM, ANN–PSO, KNN, ANN–LM with two hidden layers and bootstrap aggregation models.

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“…It was demonstrated that LSTM and gradient boosting methods were able to effectively detect the daily peak. Shirzadi et al [15] proposed a high-level framework based on machine learning and deep learning methods, i.e., SVM, random forest (RF), non-linear autoregressive exogenous (NARX), and LSTM to predict daily load for Bruce County, Canada. Temperature and wind speed were the main input features used for training the models.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach for Peak Load Forecasting: A Case Study on Panama

Ibrahim

Rabelo

2021

Energies

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Predicting the future peak demand growth becomes increasingly important as more consumer loads and electric vehicles (EVs) start connecting to the grid. Accurate forecasts will enable energy suppliers to meet demand more reliably. However, this is a challenging problem since the peak demand is very nonlinear. This study addresses the research question of how deep learning methods, such as convolutional neural networks (CNNs) and long-short term memory (LSTM) can provide better support to these areas. The goal is to build a suitable forecasting model that can accurately predict the peak demand. Several data from 2004 to 2019 was collected from Panama’s power system to validate this study. Input features such as residential consumption and monthly economic index were considered for predicting peak demand. First, we introduced three different CNN architectures which were multivariate CNN, multivariate CNN-LSTM and multihead CNN. These were then benchmarked against LSTM. We found that the CNNs outperformed LSTM, with the multivariate CNN being the best performing model. To validate our initial findings, we then evaluated the robustness of the models against Gaussian noise. We demonstrated that CNNs were far more superior than LSTM and can support spatial-temporal time series data.

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Medium-Term Regional Electricity Load Forecasting through Machine Learning and Deep Learning

Cited by 39 publications

References 37 publications

Incorporating causality in energy consumption forecasting using deep neural networks

Incorporating causality in energy consumption forecasting using deep neural networks

Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

A Deep Learning Approach for Peak Load Forecasting: A Case Study on Panama

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