Short-term forecasting of the Italian load demand during the Easter Week

Incremona, Alessandro; Nicolao, Giuseppe De

doi:10.1007/s00521-021-06797-8

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…Özdemir et al (2022) [21] applied the Artificial Bee Colony method (M-ABC) algorithms to forecast yearly energy demand in Turkey, achieving high R-squared values and low MAPE values. Incremona and Nicolao (2022) [22] utilized Gaussian Process estimators to predict electricity load demands in Italy, reporting MAPE values of 1.77%. Torres et al (2022) [23] employed Long Short-Term Memory (LSTM) models to forecast 10 min electricity demand in Spain, with MAPE values of 1.4472%.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Metaheuristic Algorithms for Optimization of Countrywide Primary Energy: Analysing Estimation and Year-Ahead Prediction

Jamil,

Serrano-Luján

2024

Energies

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In the present work, India’s primary energy use is analysed in terms of four socio-economic variables, including Gross Domestic Product, population, and the amounts of exports and imports. Historical data were obtained from the World Bank database for 44 years as annual values (1971–2014). Energy use is analysed as an optimisation problem, where a unique ensemble of two metaheuristic algorithms, Grammatical Evolution (GE), and Differential Evolution (DE), is applied. The energy optimisation problem has been investigated in two ways: estimation and a year-ahead prediction. Models are compared using RMSE (objective function) and further ranked using the Global Performance Index (GPI). For the estimation problem, RMSE values are found to be as low as 0.0078 and 0.0103 on training and test datasets, respectively. The average estimated energy use is found in good agreement with the data (RMSE = 6.3749 kgoe/capita), and the best model (E10) has an RMSE of 5.8183 kgoe/capita, with a GPI of 1.7249. For the prediction problem, RMSE is found to be 0.0096 and 0.0122 on training and test datasets, respectively. The average predicted energy use has RMSE of 7.8857 (kgoe/capita), while Model P20 has the best value of RMSE (7.9201 kgoe/capita) and a GPI of 1.8836.

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Section: Introductionmentioning

confidence: 99%

Hybrid Metaheuristic Algorithms for Optimization of Countrywide Primary Energy: Analysing Estimation and Year-Ahead Prediction

Jamil,

Serrano-Luján

2024

Energies

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“…How to use the data provided by smart meters to improve short-term load forecasting is a challenging task that will attract a great deal of attention for future research. This paper first introduces the principles of load forecasting, followed by an analysis of the need for load forecasting based on the need to carry out load forecasting work in production life [5][6]; then, a brief introduction to existing methods for load forecasting is given, with a focus on the process of implementation in load forecasting applications; the construction of a forecasting model [7][8], and finally load forecasting to obtain the results of load forecasting.…”

Section: Introductionmentioning

confidence: 99%

A weighted plain Bayesian algorithm-based method for automatic scheduling of short-term load resources in power grids

Ding

Wang

et al. 2023

International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022)

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Load forecasting is the use of historical data to build a model based on the operating characteristics of the power system, economic conditions, social conditions and natural conditions, and the use of this model to make reliable estimates of the trend of load changes in future periods. Load management is of great importance to the development of many tasks in the power system. Not only does it require comprehensive consideration of many aspects of power dispatch control, operation planning and marketing operations, but it also requires scientific forecasting of power loads in conjunction with actual practice. Short-term load forecasting is an important basis for the power supply company's scheduling plan, helping the staff to monitor the working status of the power system in real time, and is of great significance to the power supply company in reducing costs and the power plant in making power generation plans. However, the load forecasting models established by current research workers do not meet the more stringent forecasting requirements of power supply companies in terms of both accuracy and stability. The main objective of this paper is to investigate the automatic scheduling of short-term load resources for power grids based on a weighted plain Bayesian algorithm. Based on the periodicity and regularity of the historical load, this paper divides the fluctuating intervals of the sequence according to the trend of load changes, extracts the characteristics of the fluctuating intervals to represent the load intervals as electricity consumption patterns and performs density clustering. Based on the clustering results, similar load sequences are selected from the aggregated clusters to determine the reference sequences, and then a load forecasting model is established for load forecasting by combining time series and regression ideas to achieve ultra-short-term load forecasting. The test results show that the method has a good forecasting effect and provides a forecasting method based on electricity consumption patterns for power management departments.

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Graph Neural Network-Based Short‑Term Load Forecasting with Temporal Convolution

Sun,

Ning,

Shen

et al. 2023

Data Sci. Eng.

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An accurate short-term load forecasting plays an important role in modern power system’s operation and economic development. However, short-term load forecasting is affected by multiple factors, and due to the complexity of the relationships between factors, the graph structure in this task is unknown. On the other hand, existing methods do not fully aggregating data information through the inherent relationships between various factors. In this paper, we propose a short-term load forecasting framework based on graph neural networks and dilated 1D-CNN, called GLFN-TC. GLFN-TC uses the graph learning module to automatically learn the relationships between variables to solve problem with unknown graph structure. GLFN-TC effectively handles temporal and spatial dependencies through two modules. In temporal convolution module, GLFN-TC uses dilated 1D-CNN to extract temporal dependencies from historical data of each node. In densely connected residual convolution module, in order to ensure that data information is not lost, GLFN-TC uses the graph convolution of densely connected residual to make full use of the data information of each graph convolution layer. Finally, the predicted values are obtained through the load forecasting module. We conducted five studies to verify the outperformance of GLFN-TC. In short-term load forecasting, using MSE as an example, the experimental results of GLFN-TC decreased by 0.0396, 0.0137, 0.0358, 0.0213 and 0.0337 compared to the optimal baseline method on ISO-NE, AT, AP, SH and NCENT datasets, respectively. Results show that GLFN-TC can achieve higher prediction accuracy than the existing common methods.

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Short-term forecasting of the Italian load demand during the Easter Week

Cited by 5 publications

References 22 publications

Hybrid Metaheuristic Algorithms for Optimization of Countrywide Primary Energy: Analysing Estimation and Year-Ahead Prediction

Hybrid Metaheuristic Algorithms for Optimization of Countrywide Primary Energy: Analysing Estimation and Year-Ahead Prediction

A weighted plain Bayesian algorithm-based method for automatic scheduling of short-term load resources in power grids

Graph Neural Network-Based Short‑Term Load Forecasting with Temporal Convolution

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