Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review

Daut, Mohammad Azhar Mat; Hassan, Mohammad Yusri; Abdullah, Hayati; Rahman, Hasimah Abdul; Abdullah, Pauzi; Hussin, Faridah

doi:10.1016/j.rser.2016.12.015

Cited by 261 publications

(82 citation statements)

References 86 publications

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“…Similarly, Daut et al [15] published a review in 2017 with a comparison of conventional methods (e.g., times series, regression) and machine learning-based models for forecasting building electrical consumption. They too noted the improved performance with the application of machine learning-based Energies 2019, 12, 3254 3 of 27 models.…”

Section: Of 27mentioning

confidence: 99%

Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review

2019

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During the past century, energy consumption and associated greenhouse gas emissions have increased drastically due to a wide variety of factors including both technological and population-based. Therefore, increasing our energy efficiency is of great importance in order to achieve overall sustainability. Forecasting the building energy consumption is important for a wide variety of applications including planning, management, optimization, and conservation. Data-driven models for energy forecasting have grown significantly within the past few decades due to their increased performance, robustness and ease of deployment. Amongst the many different types of models, artificial neural networks rank among the most popular data-driven approaches applied to date. This paper offers a review of the studies published since the year 2000 which have applied artificial neural networks for forecasting building energy use and demand, with a particular focus on reviewing the applications, data, forecasting models, and performance metrics used in model evaluations. Based on this review, existing research gaps are identified and presented. Finally, future research directions in the area of artificial neural networks for building energy forecasting are highlighted.

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Section: Of 27mentioning

confidence: 99%

Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review

2019

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“…Machine learning (ML) based data-driven building load forecasting is based on implementations of functions deducted from samples of measured data describing the behaviour of a building load. The ML based building load forecast models have been extensively reviewed in [8][9][10][11][12][13]. Some of these models are developed for specific application areas such as building performance measurement and verification [14][15][16][17], building control [18][19][20] and demand-side management [21,22], whereas a significant number of studies are application agnostic.…”

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning Pipeline for Demand Response Capacity Scheduling

Krishnadas¹,

Kiprakis

2020

Energies

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Demand response (DR) is an integral component of smart grid operations that offers the necessary flexibility to support its decarbonisation. In incentive-based DR programs, deviations from the scheduled DR capacity affect the grid’s energy balance and result in revenue losses for the DR participants. This issue aggravates with increasing DR delivery from participants such as large consumer buildings who have limited standard methods to follow for DR capacity scheduling. Load curtailment based DR capacity availability from such consumers can be forecasted reliably with the help of supervised machine learning (ML) models. This study demonstrates the development of data-driven ML based total and flexible load forecast models for a retail building. The ML model development tasks such as data pre-processing, training-testing dataset preparation, cross-validation, algorithm selection, hyperparameter optimisation, feature ranking, model selection and model evaluation are guided by deployment-centric design criteria such as reliability, computational efficiency and scalability. Based on the selected performance metrics, the day-ahead and week-ahead ML based load forecast models developed for the retail building are shown to outperform the timeseries persistence models used for benchmarking. Furthermore, the deployment of these models for DR capacity scheduling is proposed as an ML pipeline that can be realised with the help of ML workflows, computational resources as well as systems for monitoring and visualisation. The ML pipeline ensures faster, cost-effective and large-scale deployment of forecast models that support reliable DR capacity scheduling without affecting the grid’s energy balance. Minimisation of revenue losses encourages increased DR participation from large consumer buildings, ensuring further flexibility in the smart grid.

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“…People (#) [118] 4310 5083 Passenger cars (#) [57,119,120] 2364 1846 Vans (#) 2 [57,119,120] 115 356 Trucks (#) [57,119,120] 27 3 31 4 Tractor-trailers [57,119,120] 10 12 4 Buses (#) [57,119,120] 4.1 4.5 Floor area of residential buildings (m 2 ) 5,6 [54] 183, 200 183,550 Floor area of services buildings (m 2 ) 6 [55] 92,940 38,330 Roof area available for solar electric modules (m 2 ) [125,126] 56,000 56,000 7…”

Section: Local Parameters (Based On National Statistics)mentioning

confidence: 99%

“…Internet Technology (IT) usage for demand response forecasting, scheduling, virtual power plants, and autonomous driving. Weather and electricity demand forecasting [199][200][201][202][203][204][205][206][207][208] in combination with demand response [21,26,[209][210][211] could potentially avert peaks in temporal surplus or shortage of electricity. This would reduce installed capacity cost.…”

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confidence: 99%

Fuel Cell Electric Vehicle as a Power Plant: Techno-Economic Scenario Analysis of a Renewable Integrated Transportation and Energy System for Smart Cities in Two Climates

et al. 2019

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Renewable, reliable, and affordable future power, heat, and transportation systems require efficient and versatile energy storage and distribution systems. If solar and wind electricity are the only renewable energy sources, what role can hydrogen and fuel cell electric vehicles (FCEVs) have in providing year-round 100% renewable, reliable, and affordable energy for power, heat, and transportation for smart urban areas in European climates? The designed system for smart urban areas uses hydrogen production and FCEVs through vehicle-to-grid (FCEV2G) for balancing electricity demand and supply. A techno-economic analysis was done for two technology development scenarios and two different European climates. Electricity and hydrogen supply is fully renewable and guaranteed at all times. Combining the output of thousands of grid-connected FCEVs results in large overcapacities being able to balance large deficits. Self-driving, connecting, and free-floating car-sharing fleets could facilitate vehicle scheduling. Extreme peaks in balancing never exceed more than 50% of the available FCEV2G capacity. A simple comparison shows that the cost of energy for an average household in the Mid Century scenario is affordable: 520–770 €/year (without taxes and levies), which is 65% less compared to the present fossil situation. The system levelized costs in the Mid Century scenario are 71–104 €/MWh for electricity and 2.6–3.0 €/kg for hydrogen—and we expect that further cost reductions are possible.

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Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: A review

Cited by 261 publications

References 86 publications

Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review

Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review

A Machine Learning Pipeline for Demand Response Capacity Scheduling

Fuel Cell Electric Vehicle as a Power Plant: Techno-Economic Scenario Analysis of a Renewable Integrated Transportation and Energy System for Smart Cities in Two Climates

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