A Smart Forecasting Approach to District Energy Management

Yüce, Barış; Mourshed, Monjur; Rezgui, Yacine

doi:10.3390/en10081073

Cited by 24 publications

(11 citation statements)

References 36 publications

(22 reference statements)

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“…It has many features that make it attractive for problems such as pricing options, with the capability of developing nonlinear model relationships that do not depend on the restrictive assumptions implied in the parametric approach, or on the specification of the theory that connects the prices of underlying assets to the prices of options. The implementation of an ANN model is considered successful when it has the ability to learn from the provided data and use the data in a new way [44][45][46][47][48].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus

Solyalı

2020

Sustainability

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Estimating the electricity load is a crucial task in the planning of power generation systems and the efficient operation and sustainable growth of modern electricity supply networks. Especially with the advent of smart grids, the need for fairly precise and highly reliable estimation of electricity load is greater than ever. It is a challenging task to estimate the electricity load with high precision. Many energy demand management methods are used to estimate future energy demands correctly. Machine learning methods are well adapted to the nature of the electrical load, as they can model complicated nonlinear connections through a learning process containing historical data patterns. Many scientists have used machine learning (ML) to anticipate failure before it occurs as well as predict the outcome. ML is an artificial intelligence (AI) subdomain that involves studying and developing mathematical algorithms to understand data or obtain data directly without relying on a prearranged model algorithm. ML is applied in all industries. In this paper, machine learning strategies including artificial neural network (ANN), multiple linear regression (MLR), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) were used to estimate electricity demand and propose criteria for power generation in Cyprus. The simulations were adapted to real historical data explaining the electricity usage in 2016 and 2107 with long-term and short-term analysis. It was observed that electricity load is a result of temperature, humidity, solar irradiation, population, gross national income (GNI) per capita, and the electricity price per kilowatt-hour, which provide input parameters for the ML algorithms. Using electricity load data from Cyprus, the performance of the ML algorithms was thoroughly evaluated. The results of long-term and short-term studies show that SVM and ANN are comparatively superior to other ML methods, providing more reliable and precise outcomes in terms of fewer estimation errors for Cyprus’s time series forecasting criteria for power generation.

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Section: Artificial Neural Networkmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus

Solyalı

2020

Sustainability

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“…ANN has been successfully applied for the prediction of the overall building energy consumption and also the cooling/heating demand without prior knowledge of the building geometry or the material thermal properties. [36][37][38][39][40][41][42][43]…”

Section: Forecasting Modelsmentioning

confidence: 99%

Short-term load forecasting coupled with weather profile generation methodology

Zhu

Chow

Tse

2017

Building Services Engineering Research and Technology

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Short-term building load forecasting is indispensable in daily operation of future intelligent/green buildings, particularly in formulating system control strategies and assessing the associated environmental impacts. Most previous research works have been focused on studying the advancement in forecasting techniques, but not as much on evaluating the availability of influential factors like the predicted weather profile in the coming hours. This article proposes an improved procedure to predict the building load 24 hours ahead, together with a backup weather profile generating method. The quality of the proposed weather profile generation model and the forecasting procedures were examined through a case study of application to university academic buildings. The results showed that the load forecasting accuracy with the application of either the real weather data on record or of the predicted weather data from the profile generation model is very much similar. This indicates that the weather prediction model is suitable for applying to building load forecasting. Besides, the comparisons between different sets of input data illustrated that the forecasting accuracy can be improved through the input data filtering and regrouping procedures. Practical application: A weather profile prediction technique for use in building energy forecasting was introduced. This can be coupled to a building energy use forecasting model for predicting the hourly consumption profile of the next day. This prediction time span can be crucial for formulating the daily operation plan of the utility systems or for smart micro-grid applications. The appropriateness of the methodology was evaluated through a case study.

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“…Many operations such as electricity generation control, energy planning, and security studies are based on STLF. Table 3 gives particularly a comparison between the ANN, ARIMA, and fuzzy logic methods used for STLF [30][31][32][33][34][35][36]. A review of literature highlights that the fuzzy logic approach is both sufficiently efficient and versatile to meet the expectations defined at the beginning of the article.…”

Section: The Fuzzy Logic As a Versatile Methods Used To Predict Electrmentioning

confidence: 99%

The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing

2017

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Electricity demand shifting and reduction still raise a huge interest for end-users at the household level, especially because of the ongoing design of a dynamic pricing approach. In particular, end-users must act as the starting point for decreasing their consumption during peak hours to prevent the need to extend the grid and thus save considerable costs. This article points out the relevance of a fuzzy logic algorithm to efficiently predict short term load consumption (STLC). This approach is the cornerstone of a new home energy management (HEM) algorithm which is able to optimize the cost of electricity consumption, while smoothing the peak demand. The fuzzy logic modeling involves a strong reliance on a complete database of real consumption data from many instrumented show houses. The proposed HEM algorithm enables any end-user to manage his electricity consumption with a high degree of flexibility and transparency, and "reshape" the load profile. For example, this can be mainly achieved using smart control of a storage system coupled with remote management of the electric appliances. The simulation results demonstrate that an accurate prediction of STLC gives the possibility of achieving optimal planning and operation of the HEM system.

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A Smart Forecasting Approach to District Energy Management

Cited by 24 publications

References 36 publications

A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus

A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus

Short-term load forecasting coupled with weather profile generation methodology

The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing

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