Short-Term Load Forecasting of the Greek Electricity System

Stamatellos, George; Stamatelos, A. M.

doi:10.3390/app13042719

Cited by 13 publications

(6 citation statements)

References 38 publications

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“…The authors suggested incorporating other economic indicators besides GDP that had a stronger correlation with electricity demand. Stamatellos and Stamatelos [12] suggested feed-forward neural networks for predicting electricity demand 24 h in advance for the Greek electricity system. The models incorporated important features such as the daily heating demands and cooling degree days in Athens, a representative location.…”

Section: Literature Review and Research Gap Analysismentioning

confidence: 99%

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A Holistic Approach to Power Systems Using Innovative Machine Learning and System Dynamics

et al. 2023

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The digital revolution requires greater reliability from electric power systems. However, predicting the growth of electricity demand is challenging as there is still much uncertainty in terms of demographics, industry changes, and irregular consumption patterns. Machine learning has emerged as a powerful tool, particularly with the latest developments in deep learning. Such tools can predict electricity demand and, thus, contribute to better decision-making by energy managers. However, it is important to recognize that there are no efficient methods for forecasting peak demand growth. In addition, features that add complexity, such as climate change and economic growth, take time to model. Therefore, these new tools can be integrated with other proven tools that can be used to model specific system structures, such as system dynamics. This research proposes a unique framework to support decision-makers in dealing with daily activities while attentively tracking monthly peak demand. This approach integrates advances in machine learning and system dynamics. This integration has the potential to contribute to more precise forecasts, which can help to develop strategies that can deal with supply and demand variations. A real-world case study was used to comprehend the needs of the environment and the effects of COVID-19 on power systems; it also helps to demonstrate the use of leading-edge tools, such as convolutional neural networks (CNNs), to predict electricity demand. Three well-known CNN variants were studied: a multichannel CNN, CNN-LSTM, and a multi-head CNN. This study found that the multichannel CNN outperformed all the models, with an R2 of 0.92 and a MAPE value of 1.62% for predicting the month-ahead peak demand. The multichannel CNN consists of one main model that processes four input features as a separate channel, resulting in one feature map. Furthermore, a system dynamics model was introduced to model the energy sector’s dynamic behavior (i.e., residential, commercial, and government demands, etc.). The calibrated model reproduced the historical data curve fairly well between 2005 and 2017, with an R2 value of 0.94 and a MAPE value of 4.8%.

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Section: Literature Review and Research Gap Analysismentioning

confidence: 99%

“…[11] The limited data could not adequately train the model to forecast annual energy consumption for the UK. [12] Incorporating weather parameters such as air humidity and wind speed will be considered in the future.…”

Section: Long-term Energy Consumptionmentioning

confidence: 99%

A Holistic Approach to Power Systems Using Innovative Machine Learning and System Dynamics

et al. 2023

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“…Conversely, the research conducted by [19] introduces a weighting mechanism that considers factors such as the number of inhabitants, although a direct correlation with demand is not established. This approach is similarly adopted in [24], where load forecasting is conducted for the Greek System and meteorological variable weighting is performed, also taking into account the population of a large region.…”

Section: Literature Reviewmentioning

confidence: 99%

Bottom-Up Short-Term Load Forecasting Considering Macro-Region and Weighting by Meteorological Region

Figueiró,

Abaide,

Neto

et al. 2023

Energies

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Activities related to the planning and operation of power systems use premise load forecasting, which is responsible for providing a load estimative for a given horizon that assists mainly in the operation of an electrical system. Hierarchical short-term load forecasting (STLF) becomes an approach used for this purpose, where the overall forecast is performed through system partition in smaller macro-regions and, soon after, is aggregated to compose a global forecast. In this context, this paper presents a bottom-up STLF approach for macro-regions. The main innovation is the Average Consumption per Meteorological Region (CERM) index, used to weigh the importance of each station meteorological (EM) in total load demand. Another index, the Variation of Load and Temperature (IVCT), based on historical temperature and demand changes, is proposed. These indexes are incorporated into an ANN model of the multi-layer perceptron type (MLP). The results showed a higher average performance of the index CERM and variable IVCT in relation to the other combinations performed, and the best results were used to compose the prediction of the MTR. Finally, the proposed model presented a Mean Absolute Percentage Error lower than 1%, presenting superior performance compared to an aggregate model for MTR, which shows the efficiency and contribution of the proposed methodology.

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“…The remaining heating loads were covered by a heat pump [26]. Stamatellos et al examined the interactions between short-term electric car battery storage and long-term green hydrogen storage in a building [27]. Liu et al proposed a method for sizing the PV BESS system.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Electrical and Thermal Storage in a High School Building in Central Greece

Roumpakias,

Zogou,

Stamatellou

2024

Energies

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Nearly zero-emission buildings (nZEBs) are increasingly being constructed in Europe. There are also incentives to refurbish older buildings and transform them into nZEBs. However, permission is not always granted for their connection to the grid to infuse surplus photovoltaic electricity due to the grid being overloaded with a large number of renewables. In this study, the case of a refurbished school building in Central Greece is examined. After refurbishing it, a significant amount of photovoltaic electricity surplus is observed during the summer and neutral months, which cannot be exported to the grid. The absence of an adequate battery storage capacity resulted in the rejection of an application for exporting the school’s surplus to the network and the photovoltaic installation staying idle. An alternative approach is proposed in this work, involving a shift in the export of the photovoltaic electricity surplus to the evening hours, in order for the school to be granted permission to export it to the network. To this end, an optimal battery storage size is sought by employing a building energy system simulation. The mode of operation of the battery designed for this application is set to discharge daily, in order to export the electricity surplus in the afternoon hours to the evening hours, when it is favorable for the network. Additionally, the optimal size of the thermal energy storage of the heating system is studied to further improve its energy efficiency. Our battery and storage tank size optimization study shows that a significant battery capacity is required, with 12 kWh/kWp photovoltaic panels being recommended for installation. The ever-decreasing cost of battery installations results in the net present value (NPV) of the additional investment for the battery installation becoming positive. The solution proposed forms an alternative path to further increase the penetration of renewables in saturated networks in Greece by optimizing battery storage capacity.

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Short-Term Load Forecasting of the Greek Electricity System

Cited by 13 publications

References 38 publications

A Holistic Approach to Power Systems Using Innovative Machine Learning and System Dynamics

A Holistic Approach to Power Systems Using Innovative Machine Learning and System Dynamics

Bottom-Up Short-Term Load Forecasting Considering Macro-Region and Weighting by Meteorological Region

Optimization of Electrical and Thermal Storage in a High School Building in Central Greece

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