Heating Demand Forecasting with Multiple Regression: Model Setup and Case Study

Baltputnis, Kārlis; Petrichenko, Roman; Sobolevsky, Dmitry Yu.

doi:10.1109/aieee.2018.8592144

Cited by 13 publications

(9 citation statements)

References 16 publications

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“…Let us point out that the division of time into weeks is possible in power systems that do not contain long-term energy storage plants. Detailed forecasting algorithms are described in our earlier studies [25], [26].…”

Section: Forecasting Of Processesmentioning

confidence: 99%

“…• Hydropower plant optimization software OptiBidus-HES [27]; • Thermal power plant mode calculation software OptiBidus-TEC; • Generalized energy storage software [28], which is adaptable to technologies of various kinds, as well as the software specifically intended for Kruonis pumped storage hydropower plant [29]; • Forecasting software products, for example, for water inflow [25] and heat load [26], etc.…”

Section: F the Tools To Be Usedmentioning

confidence: 99%

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Modelling the Future of the Baltic Energy Systems: A Green Scenario

Petrichenko

Sauhats

et al. 2021

Latvian Journal of Physics and Technical Sciences

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The electricity sector in Europe and in the world is undergoing rapid and profound changes. There is a sharp increase in the capacity of renewable energy sources, coal and nuclear power plants are being closed and new technologies are being introduced. Especially rapid changes are taking place in the energy systems of the Baltic States. Under these conditions, there is an emerging need for new planning tools particularly for the analysis of the power system properties in a long-term perspective. The main contribution of this article lies in the formulation and solution of optimization problems that arise when planning the development of power systems in the Baltic States. To solve this problem, it is necessary to use models of various power plants and make a number of assumptions, the justification of which requires the following actions: to briefly review the current situation of the production and demand of energy in the Baltic power systems; to conduct an overview of the Baltic interconnections and their development; to make forecasts of energy prices, water inflow, energy production and demand; to set and solve the problems of optimization of power plant operation modes; to demonstrate the possibility and limitations of the developed tools on the basis of real-life and forecast data. In this paper, a case study is performed using the main components of the overall modelling framework being developed. It focuses on the Baltic power systems in 2050 under the conditions of significant expansion in the installed capacity of renewable energy sources (RESs) and diminished fossil fuel power plant activity. The resulting electricity generation mix and trade balance with neighbouring countries is assessed, showing that even with significant RES expansion, the Baltic countries remain net importers and because of the intermittency of RESs, there are hours within the year when the demand cannot be met.

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Section: Forecasting Of Processesmentioning

confidence: 99%

Section: F the Tools To Be Usedmentioning

confidence: 99%

Modelling the Future of the Baltic Energy Systems: A Green Scenario

Petrichenko

Sauhats

et al. 2021

Latvian Journal of Physics and Technical Sciences

View full text Add to dashboard Cite

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“…The last term in the formula, ε, denotes the random error and is referred to as the residual to check the overall significance of the model and each regression coefficient [54]. The error term is independently and normally distributed, with a mean of zero and a constant variance of σ2 [55]. Regression models describe the relationships between output values and one or more input values.…”

Section: Multiple Linear Regressionmentioning

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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“…The regression coefficients were estimated according to the observations. Correlations between predictors were controlled to avoid multicollinearity problems (the correlation coefficient of the explanatory variables should not exceed 0.7) [43][44][45].…”

Section: Hypothesis 1 (H1) At Least Two Population Means Are Differentmentioning

confidence: 99%

An Analysis of the Determinants of Sport Expenditure in Sports Centers in Spain

2020

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The sports center sector is undergoing a complete transformation. Knowing users’ behavior regarding sport expenditure is needed to be able to act in terms of the future of the sector. This study analyzes the expenditure of people in Spain who are members of sports centers. Adult members were asked about their sport expenditure in several defined categories. An analysis of variance (ANOVA) and a multiple logistic regression analysis were carried out. The results show that the variables of time of participation, federation membership, and income have predictive value in terms of sport expenditure. Age and educational level also explain the significant differences in sport expenditure in its different categories. This study suggests the importance of identifying the behavior of sports center users with reference to sport expenditure as an essential component of the future strategy of the sector.

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Heating Demand Forecasting with Multiple Regression: Model Setup and Case Study

Cited by 13 publications

References 16 publications

Modelling the Future of the Baltic Energy Systems: A Green Scenario

Modelling the Future of the Baltic Energy Systems: A Green Scenario

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

An Analysis of the Determinants of Sport Expenditure in Sports Centers in Spain

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