Prediction of energy consumption of residential buildings in northern Cyprus using fuzzy interference system

Al-Shanableh, Filiz; Evcil, Ali

doi:10.1016/j.enbuild.2021.111555

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…Al-Shanableh [17] explored the feasibility of employing a Fuzzy Inference System (FIS) for predicting the energy consumption of residential buildings in northern Cyprus. Factors such as climate zone, floor area, year of construction, number of occupants, and house type were taken into account to estimate the energy consumption per unit floor area.…”

Section: Related Workmentioning

confidence: 99%

“…Most of the works focus on the consumption forecast of individual buildings (e.g., [13,14]) or an entire region of the electric grid (e.g., [11,19]), not handling the heterogeneity of buildings that may arise in an energy community, such as houses, apartments, condos, offices, retail spaces, public services, etc. Some studies consider several different dwellings in a certain area, e.g., [17], but the predicted interval is too large (an entire year in this case) for allowing a high-granular energy management of the REC system. Furthermore, the majority of the research is conducted using distinct and non-publicly available datasets, making it very difficult to properly compare the proposed solutions.…”

Section: Related Workmentioning

confidence: 99%

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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

View full text Add to dashboard Cite

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“…FIS is a soft computing technique that mimics human reasoning, allowing for the representation of uncertainty and ambiguity in the data. FIS has been used successfully to solve different nonlinear engineering problems creating a relationship between input and output parameters (Al-Shanableh et al, 2017;Al-Shanableh & Evcil, 2022). FIS modeling consists of three basic stages; fuzzification stage, fuzzy inference system (FIS), and defuzzification stage (Al-Shanableh et al, 2020).…”

Section: Fuzzy Logic Modellingmentioning

confidence: 99%

A Comparative Analysis of Uncertainty Assessment for Annual Yield Prediction of Citrus Growth Using FIS and ANFIS Models

ALSHANABLEH

2023

EPSTEM

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Accurate prediction of citrus fruit yield is essential for effective agricultural planning, resource allocation, and decision-making. This study aims to compare the uncertainty analysis of developed Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models in the context of predicting the annual yield of citrus growth. To achieve this, a comprehensive dataset comprising relevant features such as climate variables, soil conditions, and historical yield records is collected. FIS and ANFIS models were constructed using average temperature, average rainfall, and average relative humidity as input parameters and annual citrus yield as output parameters for the 1980-2019 harvesting season. Out of 40 historical data sets, 35 of them were used to train the models. The last five years were utilized for testing the proposed models. The proposed FIS and ANFIS models were found to be in close agreement with their actual counterparts, i.e. R2 values were 0.913 and 0.935 for FIS and ANFIS, respectively. To evaluate the uncertainty associated with the predictions of both models, a Monte Carlo simulation technique is employed. Preliminary results indicate that the FIS and ANFIS models exhibit promising performance in predicting the annual yield of citrus growth. However, a detailed comparison of uncertainty metrics suggests that the ANFIS model tends to provide more precise and reliable predictions, with narrower confidence intervals, than the FIS model. This could be attributed to the adaptive learning capabilities of ANFIS, allowing it to effectively capture complex nonlinear relationships between input variables and citrus yield.

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“…Fuzzy logic has also been successfully employed for the energy management of buildings, as seen in [14][15][16][17][18]. It should be noted that the use of fuzzy set theory is quite common in solving engineering problems which are more theoretical than the basic fuzzy logic control applications [19][20][21]. The big advantage of fuzzy set theory is that it can be applied to predict outputs of both linear and non-linear systems.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Logic Concept for Predicting the Seasonal Thermal Performance of Building Envelopes Based on Structural and Geographical Parameters

Kočí,

Maděra,

Khmurovska

et al. 2023

Energies

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The current practice of building thermal retrofitting is based on the outcomes of energy audits that make use of standardized tabulated information mapped on a structure or structural elements under inherently very specific conditions. Therefore, it provides very limited outcomes for further analysis, especially when decision making procedures are required to particularize the retrofitting strategy. This paper introduces a novel fuzzy logic approach for predicting the thermal performance of building walls that can be used in practice to partially substitute time-consuming and costly energy audits or complex computational analyses. The objective of this concept is to forecast the annual heating energy demands of buildings and to identify the potential energy savings that could be achieved by applying thermal retrofitting measures based on limited resources and information obtained from maps, blueprints, and/or a simple site inspection. For this purpose, a sample knowledge base was created using a validated computational model of the heat and moisture transport in the multilayered wall assemblies. Then, the fuzzy logic model was introduced to predict the thermal performance of selected walls. Our comparison of the fuzzy model outputs with simulated data proved the potential of using the proposed concept as an efficient and straightforward tool for predicting the seasonal thermal performance of building envelopes and to partially replace the current practice, which requires the utilization of building energy audits.

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Prediction of energy consumption of residential buildings in northern Cyprus using fuzzy interference system

Cited by 9 publications

References 18 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

A Comparative Analysis of Uncertainty Assessment for Annual Yield Prediction of Citrus Growth Using FIS and ANFIS Models

A Fuzzy Logic Concept for Predicting the Seasonal Thermal Performance of Building Envelopes Based on Structural and Geographical Parameters

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