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The purpose of this paper is to propose an extensiveness intelligent hybrid model to short-term load electricity forecast that can simultaneously model the seasonal complicated nonlinear uncertain patterns in the data. For this purpose, a fuzzy seasonal version of the multilayer perceptrons (MLP) is developed.

In this paper, an extended fuzzy seasonal version of classic MLP is proposed using basic concepts of seasonal modeling and fuzzy logic. The fundamental goal behind the proposed model is to improve the modeling comprehensiveness of traditional MLP in such a way that they can simultaneously model seasonal and fuzzy patterns and structures, in addition to the regular nonseasonal and crisp patterns and structures.

Eventually, the effectiveness and predictive capability of the proposed model are examined and compared with its components and some other models. Empirical results of the electricity load forecasting indicate that the proposed model can achieve more accurate and also lower risk rather than classic MLP and some other fuzzy/nonfuzzy, seasonal nonseasonal, statistical/intelligent models.

One of the most appropriate modeling tools and widely used techniques for electricity load forecasting is artificial neural networks (ANNs). The popularity of such models comes from their unique advantages such as nonlinearity, universally, generality, self-adaptively and so on. However, despite all benefits of these methods, owing to the specific features of electricity markets and also simultaneously existing different patterns and structures in the electrical data sets, they are insufficient to achieve decided forecasts, lonely. The major weaknesses of ANNs for achieving more accurate, low-risk results are seasonality and uncertainty. In this paper, the ability of the modeling seasonal and uncertain patterns has been added to other unique capabilities of traditional MLP in complex nonlinear patterns modeling.

The purpose of this study is to ascertain whether nonlinearities could be present in electricity loads observed in subtropical environments, where none or little heating is required, and whether threshold autoregressive (TAR)-type regime switching models could be advantageous in the modeling of those loads.

The actual observed load of a Brazilian regional electricity distributor from January 2013 to August 2012 was modeled using a popularly employed ARMA model for reference, and smooth and non-smooth TAR transition (non-linear) models were used as non-linear regime switching models.

Evidence of nonlinearities were found in the load series, and evidence was also found on the intrinsic resistance of this type of models to structural breaks in the data. Additionally, to reacting well to asymmetries in the data, these models avoid the use of exogenous variables. Altogether, this could prove to be a definite advantage of the use of such model alternatives.

However, even if the present work may have been limited by the observation frequency of the available data, it appears TAR models appear to be a viable alternative to forecasting short-term electricity loads. Nonetheless, additional research is required to achieve a higher accuracy of forecast data.

If such models can be successfully used, it will be a great advantage for electricity generators, as the computational effort involved in the use of such models is not significantly larger than regular linear ones.

To our knowledge, this type of research has not yet been made with subtropical/tropical electricity load data.

The purpose of this study is to present a hybrid approach to model and predict long-term energy peak load using Bayesian and Holt–Winters (HW) exponential smoothing techniques.

Bayesian inference is administered by Markov chain Monte Carlo (MCMC) sampling techniques. Machine learning tools are used to calibrate the values of the HW model parameters. Hybridization is conducted to reduce modeling uncertainty. The technique is applied to real load data. Monthly peak load forecasts are calculated as weighted averages of HW and MCMC estimates. Mean absolute percentage error and the coefficient of determination (R²) indices are used to evaluate forecasts.

The developed hybrid methodology offers advantages over both individual combined techniques and reveals more accurate and impressive results with R² above 0.97. The new technique can be used to assist energy networks in planning and implementing production projects that can ensure access to reliable and modern energy services to meet the sustainable development goal in this sector.

This is original research.

In the daily energy dispatch process in a power system, accurate short-term electricity load forecasting is a very important tool used by spot market players. It is a critical requirement for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The purpose of this study is to present an improved grey Verhulst electricity load forecasting model.

To test the effectiveness of the proposed model for short-term load forecast, studies made use of Kenya’s load demand data for the period from January 2014 to June 2019.

The convectional grey Verhulst forecasting model yielded a mean absolute percentage error of 7.82 per cent, whereas the improved model yielded much better results with an error of 2.96 per cent.

In the daily energy dispatch process in a power system, accurate short-term load forecasting is a very important tool used by spot market players. It is a critical ingredient for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The fact that the model uses actual Kenya’s utility data confirms its usefulness in the practical world for both economic planning and policy matters.

In terms of generation and transmission investments, proper load forecasting will enable utilities to make economically viable decisions. It forms a critical cog of the strategic plans for power utilities and other market players to avoid a situation of heavy stranded investment that adversely impact the final electricity prices and the other extreme scenario of expensive power shortages.

This research combined the use of natural logarithm and the exponential weighted moving average to improve the forecast accuracy of the grey Verhulst forecasting model.

The purpose of this paper is to tackle the problem an electricity trader faces when trying to set and validate his sale prices.

The solution approach consists in offering adequate incentives to the customers in order to encourage them to shift their consumptions to more favorable time periods; this is achieved by suitable price modifications. The problem of determining the most sensible prices to offer yields to a quadratic programing model which can be efficiently solved to optimality.

This paper analyses an opportunity that traders can exploit for increasing their profit margins and, in general, for setting and validating their electricity sale prices. The real case of an Italian trader has been analysed and the numerical results show that the obtained sale price modifications may produce savings, both for the trader and for his customers.

This research provides insights about the problem an electricity trader faces when setting his sale prices; it mainly focuses on the Italian market although the developed mathematical model is sufficiently general to be adopted in different scenarios.

Improving the accuracy and reducing computational costs of predictions, especially the prediction of time series, is one of the most critical parts of the decision-making processes and management in different areas and organizations. One of the best solutions to achieve high accuracy and low computational costs in time series forecasting is to develop and use efficient hybrid methods. Among the combined methods, parallel hybrid approaches are more welcomed by scholars and often have better performance than sequence ones. However, the necessary condition of using parallel combinational approaches is to estimate the appropriate weight of components. This weighting stage of parallel hybrid models is the most effective factor in forecasting accuracy as well as computational costs. In the literature, meta-heuristic algorithms have often been applied to weight components of parallel hybrid models. However, such that algorithms, despite all unique advantages, have two serious disadvantages of local optima and iterative time-consuming optimization processes. The purpose of this paper is to develop a linear optimal weighting estimator (LOWE) algorithm for finding the desired weight of components in the global non-iterative universal manner.

In this paper, a LOWE algorithm is developed to find the desired weight of components in the global non-iterative universal manner.

Empirical results indicate that the accuracy of the LOWE-based parallel hybrid model is significantly better than meta-heuristic and simple average (SA) based models. The proposed weighting approach can improve 13/96%, 11/64%, 9/35%, 25/05% the performance of the differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO) and SA-based parallel hybrid models in electricity load forecasting. While, its computational costs are considerably lower than GA, PSO and DE-based parallel hybrid models. Therefore, it can be considered as an appropriate and effective alternative weighing technique for efficient parallel hybridization for time series forecasting.

In this paper, a LOWE algorithm is developed to find the desired weight of components in the global non-iterative universal manner. Although it can be generally demonstrated that the performance of the proposed weighting technique will not be worse than the meta-heuristic algorithm, its performance is also practically evaluated in real-world data sets.

The use of energy storage devices helps the consumers to utilize the benefits and flexibilities brought by smart networks. One of the major energy storage solutions is using electric vehicle batteries. The purpose of this paper is to develop an optimal energy management strategy for a consumer connected to the power grid equipped with Vehicle-to-Home (V2H) power supply and renewable power generation unit (PV).

The problem of energy flow management is formulated and solved as an optimization problem using a linear programming model. The total energy cost of the consumer is optimized. The optimal values of decision variables are found using CPLEX solver.

The simulation results demonstrated that if the optimal decisions are made regarding the V2H operation and managing the produced power by solar panels then the total energy payments are significantly reduced.

The gap that the proposed model is trying to fill is the holistic determination of an optimal energy procurement portfolio by using various embedded resources in an optimal way. The contributions of this paper are in threefold as: first, the introduction of mobile storage devices with a periodical availability depending on driving schedules; second, offering a new business model for managing the generation of PV modules by considering the possibility of grid injection or self-consumption; third, considering Real Time Pricing in the suggested formulation.

Fuel demand forecast is a fundamental tool to guide private planning actions and public policies aim to guarantee energy supply. This paper aims to evaluate different forecasting methods to project the consumption of light fuels in Brazil (fuel used by vehicles with internal combustion engine).

Eight different methods were implemented, besides of ensemble learning technics that combine the different models. The evaluation was carried out based on the forecast error for a forecast horizon of 3, 6 and 12 months.

The statistical tests performed indicated the superiority of the evaluated models compared to a naive forecasting method. As the forecast horizon increase, the heterogeneity between the accuracy of the models becomes evident and the classification by performance becomes easier. Furthermore, for 12 months forecast, it was found methods that outperform, with statistical significance, the SARIMA method, that is widely used. Even with an unprecedented event, such as the COVID-19 crisis, the results proved to be robust.

Some regulation instruments in Brazilian fuel market requires the forecast of light fuel consumption to better deal with supply and environment issues. In that context, the level of accuracy reached allows the use of these models as tools to assist public and private agents that operate in this market.

The study seeks to fill a gap in the literature on the Brazilian light fuel market. In addition, the methodological strategy adopted assesses projection models from different areas of knowledge using a robust evaluation procedure.

This paper aims to describe the usage of a hybrid weightage-based recommender system focused on books and implementing it at an industrial level, using various recommendation approaches. Additionally, it focuses on integrating the model into the most widely used platform application.

It is an industrial level implementation of a recommendation system by applying different recommendation approaches. This study describes the usage of a hybrid weightage-based recommender system focused on books and putting a model into the most used platform application.

This paper deals with the phases of software engineering from the analysis of the requirements, the actual making of the recommender model to deployment and testing of the application at the user end. Finally, the hybridized system outperforms over other existing recommender system.

The proposed recommendation system is an industrial level implementation of a recommendation system by applying different recommendation approaches. The recommendation system is centralized to books and its recommendation. In this paper, the authors also describe the usage of a hybrid weightage-based recommender system focused on books and putting a model into the most used platform application. This paper deals with the phases of software engineering from the analysis of the requirements, the actual making of the recommender model to deployment and testing of the application at the user end. Finally, the newly created hybridized system outperforms the Netflix recommendation model as well as the Hybrid book recommendation system model as has been clearly shown in the Results Analysis section of the book. The source-code can be available at https://github.com/debajyoty/recomender-system.git.