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Purpose The aim of this paper is to suggest a new approach to the problem of sales forecasting for improving forecast accuracy. The proposed method is capable of combining, by means of appropriate weights, both the responses supplied by the best-performing conventional algorithms, which base their output on historical data, and the insights of company’s forecasters which should take account future events that are impossible to predict with traditional mathematical methods.Design/methodology/approach The authors propose a six-step methodology using multiple forecasting sources. Each of these forecasts, to consider the uncertainty of the variables involved, is expressed in the form of suitable probability density function. A proper use of the Monte Carlo Simulation allows obtaining the best fit among these different sources and to obtain a value of forecast accompanied by a probability of error known a priori.Findings The proposed approach allows the company’s demand forecasters to provide timely response to market dynamics and make a choice of weights, gradually ever more accurate, triggering a continuous process of forecast improvement. The application on a real business case proves the validity and the practical utilization of the methodology.Originality/value Forecast definition is normally entrusted to the company’s demand forecasters who often may radically modify the information suggested by the conventional prediction algorithms or, contrarily, can be too influenced by their output. This issue is the origin of the methodological approach proposed that aims to improve the forecast accuracy merging, with appropriate weights and taking into account the stochasticity involved, the outputs of sales forecast algorithms with the contributions of the company’s forecasters. [ABSTRACT FROM AUTHOR]

Combining technological solutions with investment profitability is a critical aspect in designing both traditional and innovative renewable power plants. Often, the introduction of new advanced-design solutions, although technically interesting, does not generate adequate revenue to justify their utilization. In this study, an innovative methodology is developed that aims to satisfy both targets. On the one hand, considering all of the feasible plant configurations, it allows the analysis of the investment in a stochastic regime using the Monte Carlo method. On the other hand, the impact of every technical solution on the economic performance indicators can be measured by using regression meta-models built according to the theory of Response Surface Methodology. This approach enables the design of a plant configuration that generates the best economic return over the entire life cycle of the plant. This paper illustrates an application of the proposed methodology to the evaluation of design solutions using an innovative linear Fresnel Concentrated Solar Power system. [ABSTRACT FROM AUTHOR]

This study aims at investigating the economic viability, at the pre-feasibility level, of a 5 MW electrolyser base-methanol production plant, coupled with a PV power plant. The Authors investigated the impact of different parameters, such as the PV plant size, the electrical energy cost and the components capital costs on the methanol production cost and on the system economic viability. It was also analyzed the minimum recommended sale price of the methanol in order to assure an adequate time frame for the return of the investment, considering a different combination of the investigated parameters. An economic sensitivity analysis, based on the RSM approach, was performed in order to define the most promising economic conditions under which the plant can be considered a profitable investment in terms of ARR. A guide for an economically viable plant design, allowing for the identification of the most suitable combination of the economic parameters, was proposed as a kind of "maps of existence". For the reference case, the Methanol Production Cost (MPC) resulted around 324 €/ton and the minimum methanol sale price to achieve a PBP of 10 years. The sensitivity analysis identified the cost of electricity and the capital cost of the electrolyser as the most affecting parameters for the system economic viability. In terms of ARR, the methanol price represents the most significant factor. Considering a methanol sale price ranging between 400 and 1200 €/ton, the ARR varied from 5% (20 year of PBP) to 20% (5years of PBP). From the environmental point of view, it is worth underling that the methanol production plant here proposed allows to recycle about 5800 tons of CO 2 per year and to avoid the consumption of about 5.2 MNm3 of NG per year (compared to the traditional production). • We investigate CH 3 OH synthesis from H 2 and sequestered CO 2. • We perform an economic feasibility analysis of a methanol plant coupled with RES. • We perform a sensitivity analysis on the methanol production cost and on the Pay Back Period. • We use the RSM approach to perform the sensitivity analysis. • "Maps of existence" were built to address the economic viable plant design. [ABSTRACT FROM AUTHOR]