One of the most important processes in the earlystages of construction projects is to estimate the costinvolved. This process involves a wide range of uncertainties, which make it a challenging task. Because ofunknown issues, using the experience of the experts orlooking for similar cases are the conventional methods todeal with cost estimation. The current study presents datadrivenmethods for cost estimation based on the applicationof artificial neural network (ANN) and regression models. The learning algorithms of the ANN are the Levenberg– Marquardt and the Bayesian regulated. Moreover, regressionmodels are hybridized with a genetic algorithm toobtain better estimates of the coefficients. The methods areapplied in a real case, where the input parameters of themodels are assigned based on the key issues involved in aspherical tank construction. The results reveal that while ahigh correlation between the estimated cost and the realcost exists; both ANNs could perform better than thehybridized regression models. In addition, the ANN withthe Levenberg– Marquardt learning algorithm (LMNN)obtains a better estimation than the ANN with the Bayesian-regulated learning algorithm (BRNN). The correlationbetween real data and estimated values is over 90%, whilethe mean square error is achieved around 0. 4. The proposedLMNN model can be effective to reduce uncertainty andcomplexity in the early stages of the construction project.

The current experimental research compares the performances of Solar Still (SS) with and without black-painted clay balls at Erode, India. From the investigations, it was found that an energy storage material augments the evening output. Furthermore, energy and exergy efficiencies of the SS, SS with clay balls, and SS with Black-Painted clay balls (SS with BP-clay balls) were calculated to determine the stills' performance. The distilled water output of 1.95, 2.8, and 3.43 kg was obtained from the SS, SS with clay balls, and SS with BP-clay balls, separately. The calculated energy efficiency of the SS with BP-clay balls is 19.7%, the SS with clay balls is 17%, and the SS is 14%. Also, the exergy efficiency of the SS with BP-clay balls is 1.45%, the SS with clay balls is 1.1%, and the SS is 0.86%. The distilled water output from the SS with BP-clay balls was increased by 43.2% than the SS. Experimentation proved that the use of BP-clay balls improved the performance of the SS during the evening time.

The idea of this paper is to provide a framework for simultaneous energy cost optimization and congestion management by using shared energy storage. As a case study, this paper addresses this aim by proposing the integration of a community energy storage (CES) within a distribution system, connected to four microgrids (MGs). The shared storage system enables the MGs to reduce their energy costs by optimizing the operation of the battery using a Heuristic optimization algorithm, specifically the Teaching-Learning-Based Optimization (TLBO) algorithm. Simultaneously, the distribution system operator (DSO) leverages the shared storage to alleviate congestion by purchasing charged power from the storage manager. Interestingly, the DSO is willing to pay a premium price for the charged power from the shared storage, surpassing the prevailing electricity price during congested hours. Moreover, to account for uncertainties arising from load variations and intermittent renewable energy resources (RES), Monte Carlo simulation is employed in this study. Through comprehensive simulations and analyses, the proposed approach demonstrates the potential of CES as an effective tool for congestion relief and operational cost optimization in distribution systems, and providing economic benefits to both the MGs and the DSO.