A Machine Learning Approach for Generating and Evaluating Forecasts on the Environmental Impact of the Buildings Sector

Giannelos, Spyros; Moreira, Alexandre; Papadaskalopoulos, Dimitrios; Borozan, Stefan; Pudjianto, Danny; Konstantelos, Ioannis; Sun, Mingyang; Štrbac, Goran

doi:10.3390/en16062915

Cited by 7 publications

(6 citation statements)

References 33 publications

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“…Moreover, it is of interest to the authors to compare the economic benefits resulting from the presented analysis to benefits arising from the application of other frameworks such as Backwards Induction methodology [35], machine-learning approaches [36] and stochastic optimisation [37]. Finally, the authors are interested in expanding the sensitivity analysis to include more parameters, such as changes in consumer behaviour, external economic factors and different geographical regions with varying energy demands and backgrounds.…”

Section: Discussionmentioning

confidence: 99%

Investments in Electricity Distribution Grids: Strategic versus Incremental Planning

Giannelos,

Zhang,

Pudjianto

et al. 2024

Energies

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The ongoing electrification of the transport sector is expected to cause an increase in electricity demand and, therefore, trigger significant network investments to accommodate it. This paper focuses on investment decision-making for electricity distribution grids and specifically on the strategic and incremental investment network planning approaches. In particular, the former involves network planning with the consideration of a long-term multi-stage study horizon, as opposed to a shorter–term view of the future that applies to the latter case. An investment analysis that is carried out underlines the economic savings generated from adopting a strategic investment perspective over an incremental one. These economic savings are achieved from the fact that the associated fixed investment costs are incurred only once in the horizon under strategic planning. On the other hand, incremental planning involves a series of network reinforcement decisions, thereby incurring the fixed cost multiple times. In addition, sensitivity analyses that are carried out capture the effect of key parameters, such as investment cost, discount rate and investment delay, on the generated economic savings.

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Section: Discussionmentioning

confidence: 99%

Investments in Electricity Distribution Grids: Strategic versus Incremental Planning

Giannelos,

Zhang,

Pudjianto

et al. 2024

Energies

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“…Application of Machine Learning techniques [13], such as reinforcement learning [108], may also be considered possible future work pathways [109] with a special focus on peer-to-peer trading and multi-agent microgrids [110].…”

Section: Discussionmentioning

confidence: 99%

“…Examples of the former include the Dynamic Line Rating system, Energy Storage [7], Vehicle-to-Building (V2B), Vehicle-to-Grid (V2G), Gridto-Vehicle (G2V) [8,9], Demand-Side Response (DSR) [10], Coordinated Voltage Control (CV) and Soft Open Points (SOP) [11]. Such technologies have already been deployed in various regions such as in India [12], China, Brazil, the U.K. and countries of the European Union [13].…”

Section: Introductionmentioning

confidence: 99%

Modelling Smart Grid Technologies in Optimisation Problems for Electricity Grids

et al. 2023

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The decarbonisation of the electricity grid is expected to create new electricity flows. As a result, it may require that network planners make a significant amount of investments in the electricity grids over the coming decades so as to allow the accommodation of these new flows in a way that both the thermal and voltage network constraints are respected. These investments may include a portfolio of infrastructure assets consisting of traditional technologies and smart grid technologies. One associated key challenge is the presence of uncertainty around the location, the timing, and the amount of new demand or generation connections. This uncertainty unavoidably introduces risk into the investment decision-making process as it may lead to inefficient investments and inevitably give rise to excessive investment costs. Smart grid technologies have properties that enable them to be regarded as investment options, which can allow network planners to hedge against the aforementioned uncertainty. This paper focuses on key smart technologies by providing a critical literature review and presenting the latest mathematical modelling that describes their operation.

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“…Nevertheless, these techniques showed a promising result, but these approaches have a complex architecture and demands enormous computer resources such as CNN [28], LSTM [29], and the combination between them [32][33][34]. Furthermore, it is noteworthy that a substantial portion of prior research efforts has mostly centered around datasets pertaining to residential contexts [36]. Surprisingly, scant attention has been directed towards industrial settings, despite the fact that industrial operations typically involve significantly elevated levels of active/reactive energy consumption owing to the utilization of diverse machinery and equipment.…”

Section: Introductionmentioning

confidence: 99%

Improved Active and Reactive Energy Forecasting Using a Stacking Ensemble Approach: Steel Industry Case Study

Mubarak,

Sanjari,

Stegen

et al. 2023

Energies

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The prevalence of substantial inductive/capacitive loads within the industrial sectors induces variations in reactive energy levels. The imbalance between active and reactive energy within the network leads to heightened losses, diminished network efficiency, and an associated escalation in operating costs. Therefore, the forecasting of active and reactive energy in the industrial sector confers notable advantages, including cost reduction, heightened operational efficiency, safeguarding of equipment, enhanced energy consumption management, and more effective assimilation of renewable energy sources. Consequently, a range of specialized forecasting methods for different applications have been developed to address these challenges effectively. This research proposes a stacked ensemble methodology, denoted as Stack-XGBoost, leveraging three distinct machine learning (ML) methods: extra trees regressor (ETR), adaptive boosting (AdaBoost), and random forest regressor (RFR), as foundational models. Moreover, the incorporation of an extreme gradient boosting (XGBoost) algorithm as a meta-learner serves to amalgamate the predictions generated by the base models, enhancing the precision of the active/reactive energy consumption forecasting using real time data for steel industry. To assess the efficacy of the proposed model, diverse performance metrics were employed. The results show that the proposed Stack-XGBoost model outperformed other forecasting methods. Additionally, a sensitivity analysis was conducted to assess the robustness of the proposed method against variations in input parameters.

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A Machine Learning Approach for Generating and Evaluating Forecasts on the Environmental Impact of the Buildings Sector

Cited by 7 publications

References 33 publications

Investments in Electricity Distribution Grids: Strategic versus Incremental Planning

Investments in Electricity Distribution Grids: Strategic versus Incremental Planning

Modelling Smart Grid Technologies in Optimisation Problems for Electricity Grids

Improved Active and Reactive Energy Forecasting Using a Stacking Ensemble Approach: Steel Industry Case Study

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