Several approaches of energy management systems reduce power consumption of heating demand and electricity storage based on static or dynamic tariffs. However, such methodologies impose uncertainties due to forecasting errors of energy consumption and generation, while evaluating electricity prices. Alternatively, this paper proposes a novel methodology of residential energy management to decrease electricity consumption of space-heating units and grid-connected batteries without incorporating price signals, while maintaining their characteristic operation. The proposed algorithm of energy management develops seasonal calculations of heating load and storage power to achieve energy savings in smart homes based on mixed-integer linear programming, considering photovoltaic electric generation. Power consumption of heating systems is estimated considering heat losses of conduction and ventilation through buildings in addition to other important parameters such as outdoor and indoor temperatures. Charging and discharging patterns of grid-connected batteries are modelled consistent with residential loads. Simulation results show that the proposed algorithm of energy management is able to reduce energy consumption of space-heating loads by 15%, mitigating their environmental impact while keeping their functioning usage. Moreover, the algorithm decreases charging demand of grid-connected batteries by 13%, maintaining their state-of-charge levels between 10 and 90%.

The unreasonable use of inputs, including chemical fertilizers and pesticides, has put agricultural production at risk of unsustainability in many areas. Extended exergy analysis (EEA) is an innovative method for assessing the ecological sustainability of agricultural ecosystems. EEA allows for a comprehensive assessment of the material and immaterial flows in the system and, as a result, a more accurate assessment of sustainability. In this study, a comprehensive analysis of the sustainability of autumn and spring potato systems was performed based on the EEA approach in Golestan Province in Iran during the crop year of 2017–2018. for this purpose, 120 and 60 farms were taken into account for the autumn and spring farming systems, respectively. The extended exergy (EE) values of autumn and spring potato crops in Golestan Province were 2.30E + 05 and 1.68E + 05 MJ ha⁻¹, respectively. The highest shares of EE in both autumn (57.00%) and spring (48.64%) crops were related to cumulative exergy consumption (CExC). The excessive consumption of inputs in the autumn system led to enhanced CExC. The exergy of environmental remediation cost (EE_E) for the spring farming system (7.84E + 04 MJ ha⁻¹) was lower than that of the autumn farming system (9.20E + 04 MJ ha⁻¹), which was mainly due to the high consumption of inputs like diesel fuel in the latter system. Accordingly, the ecological sustainability of the spring farming system was greater than that of the autumn farming system. The values of capital conversion factor (K_cap) for material and energy inputs to the autumn and spring farming systems were 0.011 and 0.014 US$ MJ⁻¹, respectively, which indicated that potato production in Golestan Province was more costly in the spring farming system. The values of the specific capital conversion factor of product sales (K_cap ^EE) for the autumn and spring potato systems were 0.006 and 0.005 US$ MJ⁻¹, respectively. Therefore, the economic efficiency of the autumn farming system was higher than that of the spring farming system. Also, the Extended Exergy Efficiency Indices (I]_EE) for the autumn and spring potato production systems were 45 and 37%, respectively, which represented the higher thermodynamic efficiency of the autumn farming system. The cumulative degrees of perfection for the autumn and spring potato systems were 0.78 and 0.77, respectively, which demonstrated the more optimal use of energy and materials in the autumn compared to the spring farming system. Based on the results obtained in this research, it is recommended to improve management models including selections of appropriate types and amounts of input consumptions corresponding to the systems so as to reduce costs and ameliorate thermodynamic-economic indices.

In this paper, a novel knowledge-based method is proposed for mid-term load forecasting (MTLF) in electric substations. Since knowledge-based methods rely on the similar day strategy for estimating the load profile, novel techniques are proposed to properly select these days from historical datasets. As the maintenance of substations is inevitable during some days of the year, it can affect the load shapes and make them abnormal. Therefore, some errors occur in MTLF if similar days are selected from the days with abnormal load shapes. To tackle this problem in MTLF, a pre-processing procedure based on the flag concept is conducted to separate the misleading data causing the estimation error. In addition, a new categorization of historical data is proposed in order to select days with more similar load shapes. In this procedure, the effects of neighbor substation maintenance, holidays, and special cultural days such as Ramadan are considered in order to select similar days more carefully. Finally, the hourly load profile is forecasted using a linear equation. The performance of the proposed MTLF method is evaluated in a target substation in Tehran Regional Electric Company in Iran. The results show that each of the neglecting substation maintenance days with abnormal load shapes from the similar day selection process and monthly–weekly window of data can reduce the error of forecasting by about 8%. In addition, neglecting similar days, on which the connected neighbor substations have maintenance, can reduce 5% of forecasting error. It means that these considerations are very important and can be used for improving the results of load forecasting approaches. To prove the advantage of the proposed method over other load forecasting methods, the results are compared with those of the fuzzy-based load forecasting method, which is a well-known approach. The forecasting results are improved by approximately 4% compared with those of the recent approach using the fuzzy method, which shows the applicability and superiority of the proposed method.

The photovoltaic panel cooled by a water flowing is commonly used in the study of solar cell to generate the electrical and thermal power outputs of the photovoltaic module. A practical method is therefore required for predicting the distributions of temperature and photovoltaic panel powers over time. In this study, the second-degree polynomial models were established to predict the distributions of temperature and various photovoltaic panel powers, while the linear models were used to analyse the correlation between solar power input and various photovoltaic panel powers. The results showed that the maximum values of electrical power, thermal power and power loss reached at the temperature around noontime. The same value of a photovoltaic panel power recorded at two temperatures was verified from the experiment of photovoltaic panel cooled with different cooling water flow rates. A volumetric flow rate of cooling water passing through the copper tubes determines the amount and characteristics of additional electrical power generated by the water-cooled photovoltaic panel, while a power loss in the photovoltaic panel is very sensitive to the rate of water flow. This study provides a new insight into the management of solar energy for the residential and commercial purposes in the future.