Contextual learning for energy forecasting in buildings

Jozi, Aria; Pinto, Tiago; Vale, Zita

doi:10.1016/j.ijepes.2021.107707

Cited by 8 publications

(7 citation statements)

References 27 publications

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“…Jozi et al [14] introduced a contextual learning approach for energy forecasting, with the aim of supporting decisions made by Building Energy Management Systems (BEMS). BEMS are systems designed to ensure continuous energy availability, reliability, and access for consumers.…”

Section: Related Workmentioning

confidence: 99%

“…Their proposed approach incorporates a contextual dimension that identifies various observed contexts, and groups them based on their similarities. Several machine learning techniques were employed in their study, including SVM, HyFIS [14], WM [15], and GFS.FR.MOGUL [16]. To validate the effectiveness of their approach, the researchers conducted experiments using real data on energy generation, consumption, and contextual information gathered from sensors installed in a building.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities

Matos,

Almeida,

Gonçalves

et al. 2024

Energies

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The energy sector is currently undergoing a significant shift, driven by the growing integration of renewable energy sources and the decentralization of electricity markets, which are now extending into local communities. This transformation highlights the pivotal role of prosumers within these markets, and as a result, the concept of Renewable Energy Communities is gaining traction, empowering their members to curtail reliance on non-renewable energy sources by facilitating local energy generation, storage, and exchange. Also in a community, management efficiency depends on being able to predict future consumption to make decisions regarding the purchase, sale and storage of electricity, which is why forecasting the consumption of community members is extremely important. This study presents an innovative approach to manage community energy balance, relying on Machine Learning (ML) techniques, namely eXtreme Gradient Boosting (XGBoost), to forecast electricity consumption. Subsequently, a decision algorithm is employed for energy trading with the public grid, based on solar production and energy consumption forecasts, storage levels and market electricity prices. The outcomes of the simulated model demonstrate the efficacy of incorporating these techniques, since the system showcases the potential to reduce both the community electricity expenses and its dependence on energy from the centralized distribution grid. ML-based techniques allowed better results specially for bi-hourly tariffs and high storage capacity scenarios with community bill reductions of 9.8%, 2.8% and 5.4% for high, low, and average photovoltaic (PV) generation levels, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities

Matos,

Almeida,

Gonçalves

et al. 2024

Energies

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“…(i) Contextual (DS), which refers to the consideration of users' comfort according to different contexts; (ii) Season (S), which considers a non-fully contextualized case, but still with some preference adaptation, in this case according to the season of the year; and (iii) No Context (NC), which is completely decontextualized and assumes a single illuminance preference per user. In brief, the decontextualized rule-based system NC reflects the basis rules from the work presented in [22]. For DS, users' Bref is adapted with day/night and season, for S, Bref varies according to the season, and for NC, Bref is constant throughout the year.…”

Section: Conditionsmentioning

confidence: 99%

“…For this type of system to be able to make correct decisions, it is necessary that it may be adapted to different contexts [21]. The contextualization is dependent on the problem in hand and, in this sense, several authors propose different approaches that allow context identification [21][22][23][24][25]. In [21], the authors propose the classification of contexts for energy resources using Artificial Neural Networks (ANNs).…”

Section: Introductionmentioning

confidence: 99%

Contextual Rule-Based System for Brightness Energy Management in Buildings

Ferreira,

Pinto,

Baptista

2024

Electronics

Self Cite

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The increase in renewable generation of a distributed nature has brought significant new challenges to power and energy system management and operation. Self-consumption in buildings is widespread, and with it rises the need for novel, adaptive and intelligent building energy management systems. Although there is already extensive research and development work regarding building energy management solutions, the capabilities for adaptation and contextualization of decisions are still limited. Consequently, this paper proposes a novel contextual rule-based system for energy management in buildings, which incorporates a contextual dimension that enables the adaptability of the system according to diverse contextual situations and the presence of multiple users with different preferences. Results of a case study based on real data show that the contextualization of the energy management process can maintain energy costs as low as possible, while respecting user preferences and guaranteeing their comfort.

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“…Recently, with the development of IoT and AI technologies, the building automation systems (BAS) have been transformed into information supported decision making systems. This provides ample opportunities to develop data-driven machine learning (ML) load forecasting models for building operation and control [10,15,17,18].…”

Section: Introductionmentioning

confidence: 99%

Behavior testing of load forecasting models using BuildChecks

Deng

Fan

Jiang

et al. 2022

Proceedings of the Thirteenth ACM International Conference on Future Energy Systems

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In recent years, machine learning (ML) models have been widely developed for building systems. For example, a number of ML models have been developed to predict the load demand of a building. Current ML models commonly report snap-shot accuracy only. Practitioners have difficulties in understanding how a model behaves in usage, i.e., model accuracy may change during model usage. This raises concerns in the ML-model deployment.In this paper, we propose BuildChecks, a behavior testing methodology to systematically evaluate building load forecasting ML models in usage. The challenge of developing such a methodology is to specify "what to evaluate", i.e., given a certain building load forecasting model, what tests we shall apply to this model. We categorize three model-types of the building load forecasting models and we propose three in-usage concerns. Our methodology specifies the tests, i.e., for each model-type, the in-usage concerns that should be tested. We develop an open-source BuildChecks platform to materialize our behavior testing methodology. The BuildChecks platform integrates the testing algorithms and four default realworld building datasets. We use BuildChecks to test the behaviors of two existing load forecasting models. As an example, while a ML model has high accuracy throughout all buildings, BuildChecks reports that in one building this ML-model has a cold start time of 45 days, yet in another building, the cold start time is three-fold greater, 141 days -this can lead to a delay in model usage. CCS CONCEPTS• General and reference → Evaluation.

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Contextual learning for energy forecasting in buildings

Cited by 8 publications

References 27 publications

A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities

A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities

Contextual Rule-Based System for Brightness Energy Management in Buildings

Behavior testing of load forecasting models using BuildChecks

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