In this paper, the production of low to medium temperature water for industrial process heat using solar energy is considered. In particular, the paper outlines the perspective of an optimum design method that takes into account all of the typical variables of the problem (solar irradiation, system architecture, design constraints, load type and distribution, and design and optimization criteria) and also considers the use of the fossil fuel backup system. The key element of the methodology is the definition of a synthetic combined energetic and economic utility function. This considers the attribution of an economic penalty to irreversibility in connection with the use of a fossil fuel backup. This function incorporates the share of the solar system production (solar fraction) as an optimum design variable. This paper shows how, using the proposed criteria, the optimal value of the solar fraction, defined as the share of operation of the solar system with respect to the whole energy demand, can be increased. Current practice considers values in the range between 40 and 60%. However, levels up to 80% can also be obtained with the proposed methodology. Thus, penalizing the use of fossil fuels does not exclude a priori their contribution.
The potential use of geothermal resources has been a remarkable driver for market players and companies operating in the field of geothermal energy conversion. For this reason, medium to low temperature geothermal resources have been the object of recent rise in consideration, with strong reference to the perspectives of development of Organic Rankine Cycle (ORC) technology.
In this paper, after a brief analysis of the connections between the uses of natural gas and thermal energy use, the natural gas consumption data related to Italian market are analyzed and opportunely clustered in order to compute the typical consumption profile in different days of the week in different seasons and for the different class of users: residential, tertiary and industrial. The analysis of the data shows that natural gas consumption profile is mainly related to seasonality pattern and to the weather conditions (outside temperature, humidity and wind chiller). There is also an important daily pattern related to industrial and civil sector that, at a lower degree than the previous one, does affect the consumption profile and have to be taken into account for defining an effective short and mid term thermal energy forecasting method. A possible mathematical structure of the natural gas consumption profile is provided. Due to the strong link between thermal energy use and natural gas consumption, this analysis could be considered the first step for the development of a model for thermal energy forecasting.
In this paper we use clustering algorithms to compute the typical Italian load profile in different days of the week in different seasons. This result can be exploited by energy providers to tailor more attractive time-varying tariffs for their customers. We find out that better results are obtained if the clustering is not performed directly on the data, but on some features extracted from the data. Thus, we compare some conventional features to identify the most informative ones in the Italian case.
Abstract Climate control strategies in low-automation greenhouses typically rely on the measurements from a single sensor. Indeed, implementing more complex monitoring devices and sensor networks may increase investment costs without necessarily improving profitability. This paper presents a low cost and low complexity temperature monitoring strategy, which is illustrated in a case study of tomato cultivation during the mid-season in a traditional Mediterranean greenhouse located in Pisa, Italy. The objective is to evaluate the temperature distribution within a portion of the greenhouse and to investigate the representativeness of single-point measurements for assessing the local microclimate. During the experimental campaign, the vertical and horizontal temperature differences reached maximum values of 9.9 °C and 7.3 °C, respectively. The performed temperature measurements appear to be correlated (R 2 > 0.95), and this information was exploited for the prediction of the greenhouse temperature in multiple points, resulting in an average RMSE of 1.3 °C, with differences depending on the specific position taken as reference. These findings offer insights into the representativeness of single-point measurements and the optimal positioning of the sensor station within the greenhouse. The inclusion of microclimate heterogeneity in climate control strategies can help minimise the local presence of unfavourable growth conditions, the excess of energy use, and installation and maintenance costs.
